From 1fcad4d628ec3f73a35bf3eb96d5ac7cbaff59bf Mon Sep 17 00:00:00 2001 From: research bot Date: Wed, 6 Feb 2019 21:51:52 +0000 Subject: [PATCH] updating src files --- src/default/analytic_stories.conf | 12 +- src/default/analyticstories.conf | 1148 ++++----- src/default/savedsearches.conf | 3868 ++++++++++++++--------------- 3 files changed, 2514 insertions(+), 2514 deletions(-) diff --git a/src/default/analytic_stories.conf b/src/default/analytic_stories.conf index 6605474717..e01b96a0ea 100644 --- a/src/default/analytic_stories.conf +++ b/src/default/analytic_stories.conf @@ -48,7 +48,7 @@ data_models = description = Monitor your AWS network infrastructure for bad configurations and malicious activity. Investigative searches help you probe deeper, when the facts warrant it. id = 2e8948a5-5239-406b-b56b-6c50ff268af4 version = 2.0 -mappings = {"mitre_attack": ["Command and Control", "Exfiltration", "Persistence"], "cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "nist": ["DE.DP", "DE.AE", "DE.CM", "PR.AC"]} +mappings = {"mitre_attack": ["Command and Control", "Exfiltration", "Persistence"], "cis20": ["CIS 11", "CIS 12"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "nist": ["DE.CM", "DE.AE", "DE.DP", "PR.AC"]} modification_date = 2018-05-21 reference = ["https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Appendix_NACLs.html", "https://aws.amazon.com/blogs/security/how-to-help-prepare-for-ddos-attacks-by-reducing-your-attack-surface/"] providing_technologies = ["AWS", "Splunk Enterprise Security"] @@ -199,7 +199,7 @@ category = Malware creation_date = 2019-01-29 data_models = ["Alerts", "Authentication", "Network_Traffic", "Risk", "Vulnerabilities", "Web"] description = Leverage searches that allow you to detect and investigate unusual activities that relate to the ColdRoot Remote Access Trojan that affects MacOS. An example of some of these activities are changing sensative binaries in the MacOS sub-system, detecting process names and executables associated with the RAT, detecting when a keyboard tab is installed on a MacOS machine and more. -id = ad7eb6e0-f06c-4781-b145-a42bd59c56e9 +id = bd91a2bc-d20b-4f44-a982-1bea98e86390 version = 1.0 mappings = {"mitre_attack": ["Command and Control", "Execution", "Collection", "Persistence"], "cis20": ["CIS 4", "CIS 8"], "kill_chain_phases": ["Command and Control", "Installation"], "nist": ["DE.CM", "DE.DP", "PR.PT"]} modification_date = 2019-01-29 @@ -221,7 +221,7 @@ data_models = ["Application_State", "Authentication", "Endpoint", "Network_Traff description = Monitor for and investigate activities--such as suspicious writes to the Windows Recycling Bin or email servers sending high amounts of traffic to specific hosts, for example--that may indicate that an adversary is harvesting and exfiltrating sensitive data. id = 8e03c61e-13c4-4dcd-bfbe-5ce5a8dc031a version = 1.0 -mappings = {"mitre_attack": ["Commonly Used Port", "Data Staged", "Email Collection", "Collection"], "cis20": ["CIS 7", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} +mappings = {"mitre_attack": ["Commonly Used Port", "Data Staged", "Email Collection", "Collection"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.AE", "DE.CM"]} modification_date = 2018-11-02 reference = ["https://attack.mitre.org/wiki/Collection", "https://attack.mitre.org/wiki/Technique/T1074"] providing_technologies = ["Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] @@ -698,7 +698,7 @@ data_models = ["Application_State", "Authentication", "Network_Resolution", "Net description = Detect instances of prohibited network traffic allowed in the environment, as well as protocols running on non-standard ports. Both of these types of behaviors typically violate policy and can be leveraged by attackers. id = 6d13121c-90f3-446d-8ac3-27efbbc65218 version = 1.0 -mappings = {"mitre_attack": ["Command and Control", "Exfiltration Over Command and Control Channel", "Commonly Used Port", "Exfiltration Over Alternative Protocol", "Exfiltration", "Defense Evasion"], "cis20": ["CIS 12", "CIS 13", "CIS 8", "CIS 9"], "kill_chain_phases": ["Command and Control", "Delivery", "Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "DE.CM", "PR.AC", "PR.DS"]} +mappings = {"mitre_attack": ["Command and Control", "Exfiltration Over Command and Control Channel", "Commonly Used Port", "Exfiltration Over Alternative Protocol", "Exfiltration", "Defense Evasion"], "cis20": ["CIS 12", "CIS 13", "CIS 8", "CIS 9"], "kill_chain_phases": ["Command and Control", "Actions on Objectives", "Delivery"], "nist": ["PR.PT", "DE.AE", "DE.CM", "PR.AC", "PR.DS"]} modification_date = 2018-07-24 reference = ["http://www.novetta.com/2015/02/advanced-methods-to-detect-advanced-cyber-attacks-protocol-abuse/"] providing_technologies = ["Bluecoat", "Bro", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "macOS"] @@ -898,7 +898,7 @@ data_models = description = Use the searches in this Analytic Story to monitor your AWS S3 buckets for evidence of anomalous activity and suspicious behaviors, such as detecting open S3 buckets and buckets being accessed from a new IP. The contextual and investigative searches will give you more information, when required. id = 2e8948a5-5239-406b-b56b-6c50w3168af3 version = 2.0 -mappings = {"mitre_attack": ["Exfiltration", "Credential Access", "Execution", "Initial Access"], "cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.CM", "PR.DS", "DE.DP", "PR.AC"]} +mappings = {"mitre_attack": ["Exfiltration", "Credential Access", "Execution", "Initial Access"], "cis20": ["CIS 13", "CIS 14"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "PR.DS", "DE.CM", "PR.AC"]} modification_date = 2018-11-27 reference = ["https://d0.awsstatic.com/whitepapers/aws-security-best-practices.pdf", "https://www.tripwire.com/state-of-security/security-data-protection/cloud/public-aws-s3-buckets-writable/"] providing_technologies = ["AWS", "Splunk Enterprise Security"] @@ -1039,7 +1039,7 @@ data_models = ["Application_State", "Authentication", "Change_Analysis", "Endpoi description = Monitor and detect registry changes initiated from remote locations, which can be a sign that an attacker has infiltrated your system. id = 2b1800dd-92f9-47dd-a981-fdf1351e5d55 version = 1.0 -mappings = {"mitre_attack": ["Modify Registry", "Local Port Monitor", "Application Shimming", "Lateral Movement", "Authentication Package", "Registry Run Keys / Start Folder", "AppInit DLLs", "Privilege Escalation", "Defense Evasion", "Accessibility Features", "Change Default File Association", "Persistence"], "cis20": ["CIS 5", "CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "DE.CM", "PR.AC", "PR.IP"]} +mappings = {"mitre_attack": ["Modify Registry", "Local Port Monitor", "Authentication Package", "Lateral Movement", "Application Shimming", "Registry Run Keys / Start Folder", "AppInit DLLs", "Privilege Escalation", "Defense Evasion", "Accessibility Features", "Change Default File Association", "Persistence"], "cis20": ["CIS 5", "CIS 3", "CIS 8"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["PR.PT", "DE.AE", "DE.CM", "PR.AC", "PR.IP"]} modification_date = 2018-12-03 reference = ["https://redcanary.com/blog/windows-registry-attacks-threat-detection/", "https://attack.mitre.org/wiki/Technique/T1112"] providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Splunk Enterprise Security", "Sysmon", "Tanium", "Ziften", "macOS"] diff --git a/src/default/analyticstories.conf b/src/default/analyticstories.conf index de7d434959..22df0601d6 100644 --- a/src/default/analyticstories.conf +++ b/src/default/analyticstories.conf @@ -1003,43 +1003,26 @@ known_false_positives = It is possible that these logs may be legitimately clear providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Get Process Information For Port Activity] -type = investigative -explanation = none -how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events. -known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -earliest_time_offset = 7200 -latest_time_offset = 7200 - - -[savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] +[savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] type = detection -asset_type = AWS Instance +asset_type = Endpoint +confidence = low +explanation = To detect pass the hash activity, we look at all events with event code 4624 or 4625 that specify a logon type 3 (network logons). We are looking for the NtLmSsP account, with a key length set to 0. These indicate lower level protocols that are typically used through Pass the Hash (WMI, SMB, etc.). The search also filters out events with an account name of 'Anonymous' to help reduce false positives. +how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the TA for Windows to extract EventCode, Logon_Process, Logon_Type, Key_Length and Account_Name fields from these events. +annotations = {"mitre_attack": ["Lateral Movement", "Pass the Hash"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. +providing_technologies = ["Microsoft Windows"] + + +[savedsearch://ESCU - Create or delete hidden shares using net.exe - Rule] +type = detection +asset_type = Endpoint confidence = medium -explanation = This search retrieves all the VPC Flow log entries that have recorded a blocked outbound network connection originating from your AWS environment. Then it kicks off a subsearch, which looks at the same data and performs the following series of steps: \ -\ -1. Counts the number of blocked outbound connections by each source IP\ -\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the average blocked connections, and the standard deviation for each source IP.\ -\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -\ -1. Renames `numberOfBlockedConnections` as `latestCount`.\ -\ -1. Calculates the new average value for each source IP with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation, weighting the past more heavily than the current.\ -\ -1. Updates the cache file with the latest results.\ -\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -\ -1. Filters out anything that it determines is not a spike and returns the list of source IPs to the main search. The main search subsequently gets the list of all destination IPs for which the traffic was blocked, the network interface ID, the number of unique destination IP, and the total number of blocked connections for each of these source IP addresses. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. -annotations = {"mitre_attack": ["Exfiltration", "Command and Control"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "cis20": ["CIS 11"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} -known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. -providing_technologies = ["AWS"] +explanation = Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete hidden file shares by appending "$" after the name of the share. To look for hidden shares, use a regular expression to look for a `(name_file_share)$`. In this search, we are looking for the command-line execution of net.exe with command-line parameters such as `net`, `share`, or `delete` that may correspond to the creation of hidden shares +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +annotations = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] [savedsearch://ESCU - Reg.exe used to hide files/directories via registry keys - Rule] @@ -1052,15 +1035,14 @@ known_false_positives = None at the moment providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] -type = detection -asset_type = Endpoint -confidence = low -explanation = To detect pass the hash activity, we look at all events with event code 4624 or 4625 that specify a logon type 3 (network logons). We are looking for the NtLmSsP account, with a key length set to 0. These indicate lower level protocols that are typically used through Pass the Hash (WMI, SMB, etc.). The search also filters out events with an account name of 'Anonymous' to help reduce false positives. -how_to_implement = To successfully implement this search, you must ingest your Windows Security Event logs and leverage the TA for Windows to extract EventCode, Logon_Process, Logon_Type, Key_Length and Account_Name fields from these events. -annotations = {"mitre_attack": ["Lateral Movement", "Pass the Hash"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. -providing_technologies = ["Microsoft Windows"] +[savedsearch://ESCU - Get Process Information For Port Activity] +type = investigative +explanation = none +how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events. +known_false_positives = None at this time +providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +earliest_time_offset = 7200 +latest_time_offset = 7200 [savedsearch://ESCU - TOR Traffic - Rule] @@ -1095,15 +1077,12 @@ earliest_time_offset = 3600 latest_time_offset = 3600 -[savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = This search uses the Network_Resolution data model and gathers all the answers to DNS queries for TXT records. The query then looks at the answer section and calculates the length of the answer. The search will then return information for those responses that exceed 100 characters in length. -how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. -annotations = {"mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} -known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. -providing_technologies = ["Splunk Stream", "Bro"] +[savedsearch://ESCU - Monitor Successful Backups] +type = support +explanation = This search gives you the count and the hostname of all the systems that had a successful backup each day. +how_to_implement = To successfully implement this search you must be ingesting your backup logs. +known_false_positives = None at this time +providing_technologies = ["Netbackup"] [savedsearch://ESCU - Get Logon Rights Modifications For Endpoint] @@ -1124,15 +1103,15 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Create or delete hidden shares using net.exe - Rule] +[savedsearch://ESCU - Unsuccessful Netbackup backups - Rule] type = detection asset_type = Endpoint -confidence = medium -explanation = Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete hidden file shares by appending "$" after the name of the share. To look for hidden shares, use a regular expression to look for a `(name_file_share)$`. In this search, we are looking for the command-line execution of net.exe with command-line parameters such as `net`, `share`, or `delete` that may correspond to the creation of hidden shares -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +confidence = high +explanation = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure. +how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. +annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} +known_false_positives = None identified +providing_technologies = ["Netbackup"] [savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule] @@ -1254,11 +1233,14 @@ known_false_positives = There may be other processes in your environment that us providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Baseline of Network ACL Activity by ARN] -type = support -explanation = Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `NetworkACLEvents`. -known_false_positives = None at this time +[savedsearch://ESCU - Detect S3 access from a new IP - Rule] +type = detection +asset_type = S3 Bucket +confidence = low +explanation = Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code "200") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. Next, it returns only those remote IP addresses that have first been seen accessing a specific bucket within the past hour. This is combined with the main search to return the time, bucket name, source IP, city, and country operations performed, as well as the requested URI of the resource +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. +annotations = {"mitre_attack": ["Execution", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13", "CIS 14"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour providing_technologies = ["AWS"] @@ -1273,17 +1255,6 @@ known_false_positives = Remote Desktop may be used legitimately by users on the providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Email files written outside of the Outlook directory - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\users\username\My Documents\Outlook Files\. -how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"mitre_attack": ["Collection", "Email Collection"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"]} -known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] - - [savedsearch://ESCU - Detect New Local Admin account - Rule] type = detection asset_type = Windows @@ -1295,37 +1266,15 @@ known_false_positives = The activity may be legitimate. For this reason, it's be providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule] +[savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule] type = detection -asset_type = S3 Bucket +asset_type = Endpoint confidence = medium -explanation = This search and its corresponding subsearch run through the following series of steps: \ -\ -1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for deletion of S3 buckets.\ -\ -1. Kick off a subsearch that retrieves the same data and pulls out and converts the ARN into a more friendly format.\ -\ -1. Count the number of API calls per ARN.\ -\ -1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -\ -1. Drop the count from the latest hour, since it is unnecessary, and merge the rest of the data with the results of the `stats` command. \ -\ -1. Rename `apiCalls` as `latestCount`.\ -\ -1. Calculate the new average value for each ARN with the latest count, weighting the past more heavily than the current hour. It does the same for the standard deviation—weighting the past more heavily than the current.\ -\ -1. Update the cache file with the latest results.\ -\ -1. Set the minimum threshold for the number of data points and the number of standard deviations away from the mean it must be to be considered a spike.\ -\ -1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and if the count is a sufficient number of standard deviations away from the average.\ -\ -1. Filter out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of the deleted S3 buckets, the number of unique API calls, and the total number of API calls for each of these user ARNs. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. -annotations = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. -providing_technologies = ["AWS"] +explanation = This search monitors for abnormal amounts of remote-desktop (RDP) traffic from a source to a destination that may be indicative of a brute-force attack. It does this by filtering out RDP traffic from the Network_Traffic.All_Traffic data model, using twice the standard deviation of all source-to-destination connections. If any tuple is within more than two standard deviations of all other usual RDP traffic flows, it is indicative of a brute-force attack. +how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. +annotations = {"mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "cis20": ["CIS 12", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. +providing_technologies = ["Bro", "Splunk Stream"] [savedsearch://ESCU - Web Fraud - Anomalous User Clickspeed - Rule] @@ -1371,16 +1320,6 @@ earliest_time_offset = 14400 latest_time_offset = 0 -[savedsearch://ESCU - Get EC2 Instance Details by instanceId] -type = contextual -explanation = none -how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. -known_false_positives = None at this time -providing_technologies = ["AWS"] -earliest_time_offset = 86400 -latest_time_offset = 0 - - [savedsearch://ESCU - AWS Network Access Control List Created with All Open Ports - Rule] type = detection asset_type = AWS Instance @@ -1403,17 +1342,6 @@ known_false_positives = The activity may be legitimate. PowerShell is often used providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Prohibited Network Traffic Allowed - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = The search looks for traffic marked 'is_prohibited' in the Enterprise Security lookup table 'interesting_ports_lookup', and then determines if any network devices have an associated 'allow' action on that traffic by checking the Network_Traffic data model. -how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -annotations = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "kill_chain_phases": ["Delivery", "Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE", "PR.AC"]} -known_false_positives = None identified -providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] - - [savedsearch://ESCU - Detect Unauthorized Assets by MAC address - Rule] type = detection asset_type = Infrastructure @@ -1499,15 +1427,15 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] +[savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule] type = detection -asset_type = Windows +asset_type = Endpoint confidence = medium -explanation = This search looks for Windows Event Code 4663 (object access), where the process performing the access is PowerShell.exe, the target process of the access is lsass.exe, and the access mask is given as 0x10. This is consistent with the use of PowerShell to execute Mimikatz using sekurlsa::logonpasswords. It will return the host where the activity occurred, the process and associated id, the enabled privilege, and the message in the event. -how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. -annotations = {"mitre_attack": ["Credential Access", "Credential Dumping"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} -known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. -providing_technologies = ["Microsoft Windows"] +explanation = This search uses the Network_Resolution data model and gathers all the answers to DNS queries for TXT records. The query then looks at the answer section and calculates the length of the answer. The search will then return information for those responses that exceed 100 characters in length. +how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. +annotations = {"mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} +known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. +providing_technologies = ["Splunk Stream", "Bro"] [savedsearch://ESCU - Suspicious Reg.exe Process - Rule] @@ -1540,12 +1468,15 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Count of assets by category] -type = support -explanation = This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count. -how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated. -known_false_positives = None at this time -providing_technologies = ["Splunk Enterprise Security"] +[savedsearch://ESCU - Suspicious writes to windows Recycle Bin - Rule] +type = detection +asset_type = Windows +confidence = medium +explanation = This search uses data on file writes captured via Sysmon to watch for writes to the Recycle Bin by processes other than explorer.exe. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. Next, it looks for files created with a path that includes the string "$Recycle.Bin" by processes other than explorer.exe, which is the process responsible for copying files to the Recycle Bin on delete. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. +how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +annotations = {"mitre_attack": ["Collection", "Data Staged"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} +known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. +providing_technologies = ["Sysmon"] [savedsearch://ESCU - Suspicious File Write - Rule] @@ -1591,16 +1522,6 @@ known_false_positives = It's possible that an enterprise has more than five DNS providing_technologies = ["Splunk Stream", "Bro"] -[savedsearch://ESCU - Get Sysmon WMI Activity for Host] -type = investigative -explanation = none -how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. -known_false_positives = None at this time -providing_technologies = ["Sysmon"] -earliest_time_offset = 7200 -latest_time_offset = 7200 - - [savedsearch://ESCU - Monitor Registry Keys for Print Monitors - Rule] type = detection asset_type = Endpoint @@ -1612,6 +1533,16 @@ known_false_positives = You will encounter noise from legitimate print-monitor r providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +[savedsearch://ESCU - Get Sysmon WMI Activity for Host] +type = investigative +explanation = none +how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. +known_false_positives = None at this time +providing_technologies = ["Sysmon"] +earliest_time_offset = 7200 +latest_time_offset = 7200 + + [savedsearch://ESCU - Identify Systems Creating Remote Desktop Traffic] type = support explanation = This search counts the numbers of times the system has tried to connect to another system on TCP/3389, the default port used for RDP traffic. @@ -1620,14 +1551,14 @@ known_false_positives = None at this time providing_technologies = ["Splunk Stream", "Bro"] -[savedsearch://ESCU - Get User Information from Identity Table] -type = contextual +[savedsearch://ESCU - Get All AWS Activity From Region] +type = investigative explanation = none -how_to_implement = To successfully implement this search you must have populated the identity table with information about your users. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. known_false_positives = None at this time -providing_technologies = ["Splunk Enterprise Security"] -earliest_time_offset = 864000 -latest_time_offset = 86400 +providing_technologies = ["AWS"] +earliest_time_offset = 14400 +latest_time_offset = 0 [savedsearch://ESCU - Get DNS Server History for a host] @@ -1670,15 +1601,15 @@ known_false_positives = This technique may be legitimately used by administrator providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule] +[savedsearch://ESCU - Abnormally High AWS Instances Launched by User - Rule] type = detection -asset_type = Web Server -confidence = high -explanation = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. -how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model -annotations = {"mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 12", "CIS 4", "CIS 18"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} -known_false_positives = No known false positives for this detection. -providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +asset_type = AWS Instance +confidence = medium +explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a `threshold_value` in the search. Start with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. For your reference, we then keep only the outliers and calculate the number of standard deviations away the value is from the average. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. +annotations = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} +known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. +providing_technologies = ["AWS"] [savedsearch://ESCU - Get Notable Info] @@ -1724,15 +1655,56 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] +[savedsearch://ESCU - Previously Seen AWS Regions] +type = support +explanation = In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value `awsRegion`. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_aws_regions.csv` lookup file, which will act like a baseline for detections. Please validate the entries of region names in the lookup file. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +known_false_positives = None at this time +providing_technologies = ["AWS"] + + +[savedsearch://ESCU - Detect Spike in AWS API Activity - Rule] type = detection -asset_type = Endpoint +asset_type = AWS Instance confidence = medium -explanation = Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of cmd.exe. -how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. -annotations = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +explanation = This search and its corresponding subsearch run through a series of steps, as per the following: \ +\ +1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls.\ +\ +1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ +\ +1. Counts the number of API calls per ARN.\ +\ +1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ +\ +1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ +\ +1. Renames `apiCalls` as `latestCount`.\ +\ +1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ +\ +1. Updates the cache file with the latest results.\ +\ +1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ +\ +1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ +\ +1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. +annotations = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +known_false_positives = +providing_technologies = ["AWS"] + + +[savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] +type = detection +asset_type = Windows +confidence = medium +explanation = This search looks for Windows Event Code 4663 (object access), where the process performing the access is PowerShell.exe, the target process of the access is lsass.exe, and the access mask is given as 0x10. This is consistent with the use of PowerShell to execute Mimikatz using sekurlsa::logonpasswords. It will return the host where the activity occurred, the process and associated id, the enabled privilege, and the message in the event. +how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. +annotations = {"mitre_attack": ["Credential Access", "Credential Dumping"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} +known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. +providing_technologies = ["Microsoft Windows"] [savedsearch://ESCU - WMI Temporary Event Subscription - Rule] @@ -1792,15 +1764,15 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Detect new user AWS Console Login - Rule] +[savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - Rule] type = detection -asset_type = AWS Instance -confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days -annotations = {"mitre_attack": ["Credential Access"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. -providing_technologies = ["AWS"] +asset_type = Endpoint +confidence = high +explanation = Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of **certutil.exe** with the parameters `-addcert` and `disallowed`, which add a certification to the "untrusted" certificate store. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +annotations = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] [savedsearch://ESCU - Previously seen S3 bucket access by remote IP] @@ -1822,15 +1794,14 @@ known_false_positives = It's possible that a legitimate file could be created wi providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[savedsearch://ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = This search looks for PowerShell processes that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` flag looks to see it the default execution policy of PowerShell is being overridden, while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +[savedsearch://ESCU - Get EC2 Instance Details by instanceId] +type = contextual +explanation = none +how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. +known_false_positives = None at this time +providing_technologies = ["AWS"] +earliest_time_offset = 86400 +latest_time_offset = 0 [savedsearch://ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule] @@ -1855,14 +1826,6 @@ known_false_positives = Retrieving server information may be a legitimate API re providing_technologies = ["Splunk Enterprise"] -[savedsearch://ESCU - Monitor Successful Backups] -type = support -explanation = This search gives you the count and the hostname of all the systems that had a successful backup each day. -how_to_implement = To successfully implement this search you must be ingesting your backup logs. -known_false_positives = None at this time -providing_technologies = ["Netbackup"] - - [savedsearch://ESCU - Get Emails From Specific Sender] type = investigative explanation = none @@ -1926,6 +1889,17 @@ known_false_positives = None identified providing_technologies = ["Microsoft Exchange"] +[savedsearch://ESCU - Windows hosts file modification - Rule] +type = detection +asset_type = Endpoint +confidence = high +explanation = The hosts file is present on both Windows and Linux endpoints. The purpose of the hosts file is to provide a mapping between hostnames and IP addresses, the same way DNS is used to provide such a mapping. However, the information in the hosts file takes precedence over information received via DNS and a DNS query will not be issued if the hostname of interest is found in the hosts file. As such, attackers have been observed adding entries to the host file to override any DNS resolution. For this reason, it is useful to monitor for changes to this file, which typically do not occur very often in legitimate cases. +how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. +annotations = {"mitre_attack": ["Command and Control", "Exfiltration"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 3", "CIS 8", "CIS 12"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} +known_false_positives = There may be legitimate reasons for system administrators to add entries to this file. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] + + [savedsearch://ESCU - Get Notable History] type = contextual explanation = none @@ -1962,14 +1936,15 @@ known_false_positives = None at this time providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Get Backup Logs For Endpoint] -type = contextual -explanation = none -how_to_implement = You must be ingesting your backup logs. -known_false_positives = None at this time -providing_technologies = ["Netbackup"] -earliest_time_offset = 604800 -latest_time_offset = 0 +[savedsearch://ESCU - SMB Traffic Spike - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. +how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. +annotations = {"mitre_attack": ["Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} +known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. +providing_technologies = ["Bro", "Splunk Stream"] [savedsearch://ESCU - Child Processes of Spoolsv.exe - Rule] @@ -2034,70 +2009,47 @@ known_false_positives = None identified providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Detect Spike in AWS API Activity - Rule] -type = detection -asset_type = AWS Instance -confidence = medium -explanation = This search and its corresponding subsearch run through a series of steps, as per the following: \ -\ -1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls.\ -\ -1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -\ -1. Counts the number of API calls per ARN.\ -\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -\ -1. Renames `apiCalls` as `latestCount`.\ -\ -1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ -\ -1. Updates the cache file with the latest results.\ -\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -\ -1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. -annotations = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -known_false_positives = +[savedsearch://ESCU - Get All AWS Activity From City] +type = investigative +explanation = none +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +known_false_positives = None at this time providing_technologies = ["AWS"] +earliest_time_offset = 14400 +latest_time_offset = 0 -[savedsearch://ESCU - Detect USB device insertion - Rule] +[savedsearch://ESCU - Email files written outside of the Outlook directory - Rule] type = detection asset_type = Endpoint -confidence = low -explanation = USB is a common attack vector for delivering or propagating malicious code, or the exfiltration of data. Your corporation may have a policy of not allowing removable media at all, or may only allow approved media to be used on specific hosts by specific users. By logging USB activity from Windows and other endpoints gathered using the Universal Forwarder, you can gain an understanding of what systems might be vulnerable to attack via removable media, or what users might need additional security training. This search is looking for event_id 4656 for failure and 4663 for successful USB read/write attempts from Windows Security Event logs, which is the event code generated when a files are read from and written to a removable storage device -how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. -annotations = {"mitre_attack": ["Exfiltration"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.PT", "PR.DS"]} -known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. -providing_technologies = ["Microsoft Windows"] +confidence = medium +explanation = In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\users\username\My Documents\Outlook Files\. +how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. +annotations = {"mitre_attack": ["Collection", "Email Collection"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"]} +known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Unsuccessful Netbackup backups - Rule] +[savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule] type = detection asset_type = Endpoint -confidence = high -explanation = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure. -how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. -annotations = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} +confidence = medium +explanation = This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p. The -q option performs a silent installation with no visible window, status, or warning information. The -p option allows the shim database to contain patches. It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user. +how_to_implement = To successfully implement this search, you need to ingest logs with both the process name and command-line from your endpoints. If you are using Sysmon, you will need to have a Splunk Universal Forwarder on each endpoint that you want to collect the data on. You will also need to have to deploy the Sysmon TA on these endpoints and on your search head. You must have at least version 6.0.4 of the Sysmon TA. +annotations = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} known_false_positives = None identified -providing_technologies = ["Netbackup"] +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Abnormally High AWS Instances Launched by User - Rule] +[savedsearch://ESCU - Detect malicious requests to exploit JBoss servers - Rule] type = detection -asset_type = AWS Instance -confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a `threshold_value` in the search. Start with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. For your reference, we then keep only the outliers and calculate the number of standard deviations away the value is from the average. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -providing_technologies = ["AWS"] +asset_type = Web Server +confidence = high +explanation = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. +how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model +annotations = {"mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 12", "CIS 4", "CIS 18"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} +known_false_positives = No known false positives for this detection. +providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] [savedsearch://ESCU - Detect Excessive User Account Lockouts - Rule] @@ -2143,37 +2095,44 @@ known_false_positives = The false-positive rate will vary based on how you set t providing_technologies = ["Bro", "Splunk Stream"] -[savedsearch://ESCU - Identify New User Accounts - Rule] +[savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] type = detection -asset_type = Domain Server +asset_type = AWS Instance confidence = medium -explanation = Adversaries will often seek to create new user accounts as a means of maintaining access to a target environment. Using this search, we identify accounts created in the last week by comparing the start date in the Identity_Management data model against the current time. -how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. -annotations = {"mitre_attack": ["Valid Accounts"], "cis20": ["CIS 16"], "nist": ["PR.IP"]} -known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. -providing_technologies = ["Active Directory"] - - -[savedsearch://ESCU - Detect S3 access from a new IP - Rule] -type = detection -asset_type = S3 Bucket -confidence = low -explanation = Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code "200") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. Next, it returns only those remote IP addresses that have first been seen accessing a specific bucket within the past hour. This is combined with the main search to return the time, bucket name, source IP, city, and country operations performed, as well as the requested URI of the resource -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. -annotations = {"mitre_attack": ["Execution", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13", "CIS 14"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour +explanation = This search retrieves all the VPC Flow log entries that have recorded a blocked outbound network connection originating from your AWS environment. Then it kicks off a subsearch, which looks at the same data and performs the following series of steps: \ +\ +1. Counts the number of blocked outbound connections by each source IP\ +\ +1. Loads the cache file that contains the number of data points, the count from the latest hour, the average blocked connections, and the standard deviation for each source IP.\ +\ +1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ +\ +1. Renames `numberOfBlockedConnections` as `latestCount`.\ +\ +1. Calculates the new average value for each source IP with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation, weighting the past more heavily than the current.\ +\ +1. Updates the cache file with the latest results.\ +\ +1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ +\ +1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ +\ +1. Filters out anything that it determines is not a spike and returns the list of source IPs to the main search. The main search subsequently gets the list of all destination IPs for which the traffic was blocked, the network interface ID, the number of unique destination IP, and the total number of blocked connections for each of these source IP addresses. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. +annotations = {"mitre_attack": ["Exfiltration", "Command and Control"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "cis20": ["CIS 11"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} +known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. providing_technologies = ["AWS"] -[savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule] +[savedsearch://ESCU - Detect USB device insertion - Rule] type = detection asset_type = Endpoint -confidence = medium -explanation = This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p. The -q option performs a silent installation with no visible window, status, or warning information. The -p option allows the shim database to contain patches. It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user. -how_to_implement = To successfully implement this search, you need to ingest logs with both the process name and command-line from your endpoints. If you are using Sysmon, you will need to have a Splunk Universal Forwarder on each endpoint that you want to collect the data on. You will also need to have to deploy the Sysmon TA on these endpoints and on your search head. You must have at least version 6.0.4 of the Sysmon TA. -annotations = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -known_false_positives = None identified -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +confidence = low +explanation = USB is a common attack vector for delivering or propagating malicious code, or the exfiltration of data. Your corporation may have a policy of not allowing removable media at all, or may only allow approved media to be used on specific hosts by specific users. By logging USB activity from Windows and other endpoints gathered using the Universal Forwarder, you can gain an understanding of what systems might be vulnerable to attack via removable media, or what users might need additional security training. This search is looking for event_id 4656 for failure and 4663 for successful USB read/write attempts from Windows Security Event logs, which is the event code generated when a files are read from and written to a removable storage device +how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. +annotations = {"mitre_attack": ["Exfiltration"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.PT", "PR.DS"]} +known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. +providing_technologies = ["Microsoft Windows"] [savedsearch://ESCU - Detect Large Outbound ICMP Packets - Rule] @@ -2197,15 +2156,15 @@ earliest_time_offset = 86400 latest_time_offset = 86400 -[savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - Rule] +[savedsearch://ESCU - Attempt To Stop Security Service - Rule] type = detection -asset_type = AWS Instance -confidence = medium -explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a `threshold_value` in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier to 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. We then filter out outliers with a value of 1 and show only those instance-termination events that happened within the previous 10 minutes. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -annotations = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. -providing_technologies = ["AWS"] +asset_type = Endpoint +confidence = high +explanation = This search looks for the processes **net.exe** and **sc.exe** with a parameter of `"stop"`. It then searches a list of security-related services included in a lookup file for matches on the command line. Results are subsequently returned in table format. The included lookup file can be modified to update the services to monitor. +how_to_implement = You must be ingesting logs with both the process name and command line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., +annotations = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] [savedsearch://ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule] @@ -2257,14 +2216,14 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Detect new API calls from user roles - Rule] +[savedsearch://ESCU - Detect New Open S3 buckets - Rule] type = detection -asset_type = AWS Instance +asset_type = S3 Bucket confidence = medium -explanation = The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is `AssumedRole`. It then appends the historical data to those results in the lookup file. Next, it recalculates the `earliest` and `latest` fields for each user role, as well as the name of the API call, and returns only those roles and API calls that have first been seen in the past hour. This is combined with the main search to return the values of API calls, name of the user role, and the earliest and latest time of this activity. It is worth noting that the name of the role of a particular user is parsed as "userName" in the CloudTrail logs. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. -annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. +explanation = This search queries CloudTrail logs for events with S3 bucket access controls given to the "All Users" group, which allows anyone in the world access to the resource. This search generates a table displaying the time when the bucket was made public, the permission of the S3 bucket, the bucket name, and the ARN of the user who created the bucket. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail inputs. The threshold value should be tuned to your environment. +annotations = {"mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. providing_technologies = ["AWS"] @@ -2289,14 +2248,17 @@ known_false_positives = It's possible that legitimate traffic will have long URL providing_technologies = ["Splunk Stream", "Bro"] -[savedsearch://ESCU - Get Web Session Information via session_id] -type = investigative -explanation = none -how_to_implement = This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server. -known_false_positives = None at this time -providing_technologies = ["Splunk Stream"] -earliest_time_offset = 3600 -latest_time_offset = 3600 +[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] +type = detection +asset_type = AWS Instance +confidence = medium +explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. +providing_technologies = ["AWS"] [savedsearch://ESCU - Execution of File With Spaces Before Extension - Rule] @@ -2331,16 +2293,6 @@ known_false_positives = There are many legitimate applications that must execute providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[savedsearch://ESCU - Get All AWS Activity From Region] -type = investigative -explanation = none -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time -providing_technologies = ["AWS"] -earliest_time_offset = 14400 -latest_time_offset = 0 - - [savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - Rule] type = detection asset_type = AWS Instance @@ -2373,17 +2325,6 @@ known_false_positives = It is possible that an administrator created and deleted providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Remote WMI Command Attempt - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -known_false_positives = Administrators may use this legitimately to gather info from remote systems. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] - - [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] type = detection asset_type = AWS Instance @@ -2429,15 +2370,15 @@ earliest_time_offset = 86400 latest_time_offset = 0 -[savedsearch://ESCU - Common Ransomware Extensions - Rule] +[savedsearch://ESCU - Detect new user AWS Console Login - Rule] type = detection -asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated by ransomware. -how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"mitre_attack": [], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +asset_type = AWS Instance +confidence = medium +explanation = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days +annotations = {"mitre_attack": ["Credential Access"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.AE"]} +known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. +providing_technologies = ["AWS"] [savedsearch://ESCU - EC2 Instance Started With Previously Unseen User - Rule] @@ -2451,14 +2392,6 @@ known_false_positives = It's possible that a user will start to create EC2 insta providing_technologies = ["AWS"] -[savedsearch://ESCU - Identify Systems Using Remote Desktop] -type = support -explanation = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. It does this by looking for the process in the Application_State data model. -how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. -known_false_positives = None at this time -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] - - [savedsearch://ESCU - Previously seen command line arguments] type = support explanation = In this support search, we look for command-line arguments using the parameter `/c` to execute processes and create an initial baseline cache for the previous 30 days. This will include the earliest and latest times a particular command-line argument is seen in our dataset, grouped by the command-line value. @@ -2467,15 +2400,15 @@ known_false_positives = None at this time providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - AWS Network Access Control List Deleted - Rule] +[savedsearch://ESCU - Remote WMI Command Attempt - Rule] type = detection -asset_type = AWS Instance +asset_type = Endpoint confidence = medium -explanation = The search looks for CloudTrail events to detect whether any network ACLs have been deleted and gives you values of error messages and error codes (if any), user details, user source IP, the user who initiated this request, and the name of the event. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -annotations = {"mitre_attack": ["Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = It's possible that a user has legitimately deleted a network ACL. -providing_technologies = ["AWS"] +explanation = Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +annotations = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +known_false_positives = Administrators may use this legitimately to gather info from remote systems. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] [savedsearch://ESCU - Process Execution via WMI - Rule] @@ -2489,15 +2422,25 @@ known_false_positives = Although unlikely, administrators may use wmi to execute providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] +[savedsearch://ESCU - Get User Information from Identity Table] +type = contextual +explanation = none +how_to_implement = To successfully implement this search you must have populated the identity table with information about your users. +known_false_positives = None at this time +providing_technologies = ["Splunk Enterprise Security"] +earliest_time_offset = 864000 +latest_time_offset = 86400 + + +[savedsearch://ESCU - AWS Network Access Control List Deleted - Rule] type = detection -asset_type = Endpoint +asset_type = AWS Instance confidence = medium -explanation = Clients should be resolving their DNS requests via a trusted DNS server. This search will identify DNS queries being sent to unauthorized DNS servers by comparing the destination and source of the traffic with assets marked as DNS servers. -how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. -annotations = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} -known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. -providing_technologies = ["Splunk Stream", "Bro"] +explanation = The search looks for CloudTrail events to detect whether any network ACLs have been deleted and gives you values of error messages and error codes (if any), user details, user source IP, the user who initiated this request, and the name of the event. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +annotations = {"mitre_attack": ["Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11"], "nist": ["DE.DP", "DE.AE"]} +known_false_positives = It's possible that a user has legitimately deleted a network ACL. +providing_technologies = ["AWS"] [savedsearch://ESCU - Get All AWS Activity From Country] @@ -2551,15 +2494,22 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Schtasks scheduling job on remote system - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled on a remote host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or malicious executables on remote systems. -how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task", "Remote Services"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} -known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +[savedsearch://ESCU - Baseline of Network ACL Activity by ARN] +type = support +explanation = Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `NetworkACLEvents`. +known_false_positives = None at this time +providing_technologies = ["AWS"] + + +[savedsearch://ESCU - Get Authentication Logs For Endpoint] +type = contextual +explanation = none +how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. +known_false_positives = None at this time +providing_technologies = ["Microsoft Windows", "Linux", "macOS"] +earliest_time_offset = 43200 +latest_time_offset = 1 [savedsearch://ESCU - Detect web traffic to dynamic domain providers - Rule] @@ -2583,12 +2533,15 @@ known_false_positives = The wevtutil.exe application is a legitimate Windows eve providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Previously Seen EC2 AMIs] -type = support -explanation = In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv file. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time -providing_technologies = ["AWS"] +[savedsearch://ESCU - Prohibited Network Traffic Allowed - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = The search looks for traffic marked 'is_prohibited' in the Enterprise Security lookup table 'interesting_ports_lookup', and then determines if any network devices have an associated 'allow' action on that traffic by checking the Network_Traffic data model. +how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. +annotations = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "kill_chain_phases": ["Delivery", "Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE", "PR.AC"]} +known_false_positives = None identified +providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] [savedsearch://ESCU - Detect Rare Executables - Rule] @@ -2623,12 +2576,23 @@ known_false_positives = None at this time providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -[savedsearch://ESCU - Identify Systems Receiving Remote Desktop Traffic] +[savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] +type = detection +asset_type = AWS Instance +confidence = medium +explanation = The subsearch returns the instance types of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the earliest seen time field for each instance type and returns only those instance types that has first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. +providing_technologies = ["AWS"] + + +[savedsearch://ESCU - Baseline of API Calls per User ARN] type = support -explanation = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic. -how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it. +explanation = This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each ARN had. This table is then stored in a lookup file. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro"] +providing_technologies = ["AWS"] [savedsearch://ESCU - Sc.exe Manipulating Windows Services - Rule] @@ -2642,15 +2606,48 @@ known_false_positives = Using sc.exe to manipulate Windows services is uncommon. providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Remote Desktop Network Bruteforce - Rule] +[savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = This search monitors for abnormal amounts of remote-desktop (RDP) traffic from a source to a destination that may be indicative of a brute-force attack. It does this by filtering out RDP traffic from the Network_Traffic.All_Traffic data model, using twice the standard deviation of all source-to-destination connections. If any tuple is within more than two standard deviations of all other usual RDP traffic flows, it is indicative of a brute-force attack. -how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. -annotations = {"mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "cis20": ["CIS 12", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. -providing_technologies = ["Bro", "Splunk Stream"] +explanation = Clients should be resolving their DNS requests via a trusted DNS server. This search will identify DNS queries being sent to unauthorized DNS servers by comparing the destination and source of the traffic with assets marked as DNS servers. +how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. +annotations = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. +providing_technologies = ["Splunk Stream", "Bro"] + + +[savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule] +type = detection +asset_type = S3 Bucket +confidence = medium +explanation = This search and its corresponding subsearch run through the following series of steps: \ +\ +1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for deletion of S3 buckets.\ +\ +1. Kick off a subsearch that retrieves the same data and pulls out and converts the ARN into a more friendly format.\ +\ +1. Count the number of API calls per ARN.\ +\ +1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ +\ +1. Drop the count from the latest hour, since it is unnecessary, and merge the rest of the data with the results of the `stats` command. \ +\ +1. Rename `apiCalls` as `latestCount`.\ +\ +1. Calculate the new average value for each ARN with the latest count, weighting the past more heavily than the current hour. It does the same for the standard deviation—weighting the past more heavily than the current.\ +\ +1. Update the cache file with the latest results.\ +\ +1. Set the minimum threshold for the number of data points and the number of standard deviations away from the mean it must be to be considered a spike.\ +\ +1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and if the count is a sufficient number of standard deviations away from the average.\ +\ +1. Filter out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of the deleted S3 buckets, the number of unique API calls, and the total number of API calls for each of these user ARNs. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. +annotations = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. +providing_technologies = ["AWS"] [savedsearch://ESCU - Deleting Shadow Copies - Rule] @@ -2695,15 +2692,12 @@ known_false_positives = Administrators may create jobs on systems forcing reboot providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Suspicious writes to windows Recycle Bin - Rule] -type = detection -asset_type = Windows -confidence = medium -explanation = This search uses data on file writes captured via Sysmon to watch for writes to the Recycle Bin by processes other than explorer.exe. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. Next, it looks for files created with a path that includes the string "$Recycle.Bin" by processes other than explorer.exe, which is the process responsible for copying files to the Recycle Bin on delete. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. -how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -annotations = {"mitre_attack": ["Collection", "Data Staged"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. -providing_technologies = ["Sysmon"] +[savedsearch://ESCU - Count of assets by category] +type = support +explanation = This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count. +how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated. +known_false_positives = None at this time +providing_technologies = ["Splunk Enterprise Security"] [savedsearch://ESCU - Get Email Info] @@ -2732,6 +2726,28 @@ known_false_positives = None at this time providing_technologies = ["AWS"] +[savedsearch://ESCU - Common Ransomware Extensions - Rule] +type = detection +asset_type = Endpoint +confidence = high +explanation = This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated by ransomware. +how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. +annotations = {"mitre_attack": [], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] + + +[savedsearch://ESCU - Abnormally High AWS Instances Terminated by User - Rule] +type = detection +asset_type = AWS Instance +confidence = medium +explanation = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a `threshold_value` in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier to 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. We then filter out outliers with a value of 1 and show only those instance-termination events that happened within the previous 10 minutes. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +annotations = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} +known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. +providing_technologies = ["AWS"] + + [savedsearch://ESCU - Extended Period Without Successful Netbackup Backups - Rule] type = detection asset_type = Endpoint @@ -2810,6 +2826,17 @@ known_false_positives = There are no known false positives. providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"] +[savedsearch://ESCU - Registry Keys for Creating SHIM Databases - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor. +how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. +annotations = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] + + [savedsearch://ESCU - Web Fraud - Account Harvesting - Rule] type = detection asset_type = Account @@ -2831,35 +2858,6 @@ earliest_time_offset = 3600 latest_time_offset = 3600 -[savedsearch://ESCU - Get All AWS Activity From City] -type = investigative -explanation = none -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time -providing_technologies = ["AWS"] -earliest_time_offset = 14400 -latest_time_offset = 0 - - -[savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = This search looks for PowerShell processes that are passing encoded commands on the command-line. The flags "-EncodedCommand" and "-enc" are two different possible flags that can be used to pass base64 encoded commands to PowerShell. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = System administrators may use this option, but it's not common. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] - - -[savedsearch://ESCU - Add Prohibited Processes to Enterprise Security] -type = support -explanation = This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries. -how_to_implement = This search should be run on each new install of ESCU. -known_false_positives = None at this time -providing_technologies = ["Splunk Enterprise Security"] - - [savedsearch://ESCU - Monitor Email For Brand Abuse - Rule] type = detection asset_type = Endpoint @@ -2879,14 +2877,15 @@ known_false_positives = None at this time providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Get Authentication Logs For Endpoint] -type = contextual -explanation = none -how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. -known_false_positives = None at this time -providing_technologies = ["Microsoft Windows", "Linux", "macOS"] -earliest_time_offset = 43200 -latest_time_offset = 1 +[savedsearch://ESCU - Schtasks scheduling job on remote system - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled on a remote host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or malicious executables on remote systems. +how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +annotations = {"mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task", "Remote Services"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} +known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] [savedsearch://ESCU - Single Letter Process On Endpoint - Rule] @@ -2900,6 +2899,14 @@ known_false_positives = Single-letter executables are not always malicious. Inve providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +[savedsearch://ESCU - Previously Seen EC2 AMIs] +type = support +explanation = In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv file. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +known_false_positives = None at this time +providing_technologies = ["AWS"] + + [savedsearch://ESCU - Protocols passing authentication in cleartext - Rule] type = detection asset_type = Endpoint @@ -2911,6 +2918,16 @@ known_false_positives = Some networks may use kerberized FTP or telnet servers, providing_technologies = ["Splunk Stream", "Bro"] +[savedsearch://ESCU - Get Web Session Information via session_id] +type = investigative +explanation = none +how_to_implement = This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server. +known_false_positives = None at this time +providing_technologies = ["Splunk Stream"] +earliest_time_offset = 3600 +latest_time_offset = 3600 + + [savedsearch://ESCU - Processes created by netsh - Rule] type = detection asset_type = Endpoint @@ -2922,14 +2939,12 @@ known_false_positives = It is unusual for netsh.exe to have any child processes providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Get First Occurrence and Last Occurrence of a MAC Address] -type = contextual -explanation = none -how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. +[savedsearch://ESCU - Add Prohibited Processes to Enterprise Security] +type = support +explanation = This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries. +how_to_implement = This search should be run on each new install of ESCU. known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro", "Microsoft Windows"] -earliest_time_offset = 864000 -latest_time_offset = 86400 +providing_technologies = ["Splunk Enterprise Security"] [savedsearch://ESCU - Monitor DNS For Brand Abuse - Rule] @@ -2943,15 +2958,14 @@ known_false_positives = None at this time providing_technologies = ["Splunk Stream", "Bro"] -[savedsearch://ESCU - Remote Desktop Network Traffic - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = This search finds systems that do not commonly communicate use remote desktop. It does this by filtering out all systems that have the "common_rdp_source" or "common_rdp_destination" category applied to that system. Categories are applied to systems using the Assets and Identity framework. -how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. -annotations = {"mitre_attack": ["Lateral Movement", "Remote Desktop Protocol", "Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -known_false_positives = Remote Desktop may be used legitimately by users on the network. -providing_technologies = ["Bro", "Splunk Stream"] +[savedsearch://ESCU - Get First Occurrence and Last Occurrence of a MAC Address] +type = contextual +explanation = none +how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. +known_false_positives = None at this time +providing_technologies = ["Splunk Stream", "Bro", "Microsoft Windows"] +earliest_time_offset = 864000 +latest_time_offset = 86400 [savedsearch://ESCU - Create local admin accounts using net.exe - Rule] @@ -3005,12 +3019,15 @@ known_false_positives = None identified. providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Baseline of API Calls per User ARN] -type = support -explanation = This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each ARN had. This table is then stored in a lookup file. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time -providing_technologies = ["AWS"] +[savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = This search looks for PowerShell processes that are passing encoded commands on the command-line. The flags "-EncodedCommand" and "-enc" are two different possible flags that can be used to pass base64 encoded commands to PowerShell. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +annotations = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +known_false_positives = System administrators may use this option, but it's not common. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] [savedsearch://ESCU - Get EC2 Launch Details] @@ -3023,34 +3040,51 @@ earliest_time_offset = 7200 latest_time_offset = 0 -[savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - Rule] +[savedsearch://ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule] type = detection -asset_type = Endpoint -confidence = high -explanation = Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of **certutil.exe** with the parameters `-addcert` and `disallowed`, which add a certification to the "untrusted" certificate store. -how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -annotations = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] - - -[savedsearch://ESCU - Previously Seen AWS Regions] -type = support -explanation = In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value `awsRegion`. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_aws_regions.csv` lookup file, which will act like a baseline for detections. Please validate the entries of region names in the lookup file. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -known_false_positives = None at this time +asset_type = AWS Instance +confidence = medium +explanation = This search\ +\ +1. Retrieves the **AssumeRole** event\ +\ +1. Verifies that the log entry contains a value for the account ID of the requesting account\ +\ +1. Ensures that the requesting account ID does not match the account ID of the requested account\ +\ +1. Pulls in the previously seen requesting and requested account IDs\ +\ +1. Splits up and executes multiple search paths at the same.\ +\ +1. The first path determines the **firstTime** and **lastTime** entries for the cache file\ +\ +1. Outputs the data to the cache file.\ +\ +1. Creates a conditional statement that is always false (both because we don't want these values to exit the search pipeline and because we think we're clever).The second pipeline adds the **firstTime** and **lastTime** entries to search results. Next, it filters out any account pairs that haven't been seen for the first time within the last hour. The `isnotnull(_time)` will remove the entries from the cache file.\ +\ +The search finishes by gathering the data that it will display to the user. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search. +annotations = {"mitre_attack": ["Credential Access"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} +known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. providing_technologies = ["AWS"] -[savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule] -type = detection -asset_type = Endpoint -confidence = high -explanation = This search looks at any emails with file attachment names that contain many spaces relative to the length of the file name. Specifically, it checks if spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will then output the message ID of the email, the count, the recipient address and the recipient user, first and last time this event was seen and the space ratio of the file attachment name. -how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. -annotations = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 7"], "nist": ["PR.IP"]} +[savedsearch://ESCU - Get Backup Logs For Endpoint] +type = contextual +explanation = none +how_to_implement = You must be ingesting your backup logs. known_false_positives = None at this time -providing_technologies = ["Microsoft Exchange"] +providing_technologies = ["Netbackup"] +earliest_time_offset = 604800 +latest_time_offset = 0 + + +[savedsearch://ESCU - Baseline of blocked outbound traffic from AWS] +type = support +explanation = Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each source IP had. This table is then stored in a lookup file. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`. +known_false_positives = None at this time +providing_technologies = ["AWS"] [savedsearch://ESCU - Previously seen users in CloudTrail] @@ -3071,17 +3105,6 @@ earliest_time_offset = 14400 latest_time_offset = 0 -[savedsearch://ESCU - Detect New Open S3 buckets - Rule] -type = detection -asset_type = S3 Bucket -confidence = medium -explanation = This search queries CloudTrail logs for events with S3 bucket access controls given to the "All Users" group, which allows anyone in the world access to the resource. This search generates a table displaying the time when the bucket was made public, the permission of the S3 bucket, the bucket name, and the ARN of the user who created the bucket. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -annotations = {"mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. -providing_technologies = ["AWS"] - - [savedsearch://ESCU - Get DNS traffic ratio] type = investigative explanation = none @@ -3114,14 +3137,6 @@ known_false_positives = It is likely that the outbound Server Message Block (SMB providing_technologies = ["Bro", "Splunk Stream"] -[savedsearch://ESCU - Count of Unique IPs Connecting to Ports] -type = support -explanation = For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections. -how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. -known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro"] - - [savedsearch://ESCU - Detect Spike in Security Group Activity - Rule] type = detection asset_type = AWS Instance @@ -3188,6 +3203,17 @@ known_false_positives = A previously unseen service is not necessarily malicious providing_technologies = ["Microsoft Windows"] +[savedsearch://ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = This search looks for PowerShell processes that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` flag looks to see it the default execution policy of PowerShell is being overridden, while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +annotations = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] + + [savedsearch://ESCU - Registry Keys Used For Persistence - Rule] type = detection asset_type = Endpoint @@ -3199,15 +3225,15 @@ known_false_positives = There are many legitimate applications that must execute providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[savedsearch://ESCU - SMB Traffic Spike - Rule] +[savedsearch://ESCU - Batch File Write to System32 - Rule] type = detection asset_type = Endpoint -confidence = medium -explanation = Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. -how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. -annotations = {"mitre_attack": ["Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. -providing_technologies = ["Bro", "Splunk Stream"] +confidence = high +explanation = This search looks at file modifications across your hosts, as well as for evidence of batch files being written to paths that include "system32." This activity is consistent with some SamSam attacks and is, in general, suspicious. +how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. +annotations = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] [savedsearch://ESCU - AWS Investigate User Activities By AccessKeyId] @@ -3242,25 +3268,22 @@ known_false_positives = As is common with many fraud-related searches, we are us providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Bro"] -[savedsearch://ESCU - Windows hosts file modification - Rule] +[savedsearch://ESCU - Identify New User Accounts - Rule] type = detection -asset_type = Endpoint -confidence = high -explanation = The hosts file is present on both Windows and Linux endpoints. The purpose of the hosts file is to provide a mapping between hostnames and IP addresses, the same way DNS is used to provide such a mapping. However, the information in the hosts file takes precedence over information received via DNS and a DNS query will not be issued if the hostname of interest is found in the hosts file. As such, attackers have been observed adding entries to the host file to override any DNS resolution. For this reason, it is useful to monitor for changes to this file, which typically do not occur very often in legitimate cases. -how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -annotations = {"mitre_attack": ["Command and Control", "Exfiltration"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 3", "CIS 8", "CIS 12"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} -known_false_positives = There may be legitimate reasons for system administrators to add entries to this file. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +asset_type = Domain Server +confidence = medium +explanation = Adversaries will often seek to create new user accounts as a means of maintaining access to a target environment. Using this search, we identify accounts created in the last week by comparing the start date in the Identity_Management data model against the current time. +how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. +annotations = {"mitre_attack": ["Valid Accounts"], "cis20": ["CIS 16"], "nist": ["PR.IP"]} +known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. +providing_technologies = ["Active Directory"] -[savedsearch://ESCU - Attempt To Stop Security Service - Rule] -type = detection -asset_type = Endpoint -confidence = high -explanation = This search looks for the processes **net.exe** and **sc.exe** with a parameter of `"stop"`. It then searches a list of security-related services included in a lookup file for matches on the command line. Results are subsequently returned in table format. The included lookup file can be modified to update the services to monitor. -how_to_implement = You must be ingesting logs with both the process name and command line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., -annotations = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. +[savedsearch://ESCU - Identify Systems Using Remote Desktop] +type = support +explanation = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. It does this by looking for the process in the Application_State data model. +how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. +known_false_positives = None at this time providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] @@ -3297,46 +3320,39 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Registry Keys for Creating SHIM Databases - Rule] +[savedsearch://ESCU - Remote Desktop Network Traffic - Rule] type = detection asset_type = Endpoint confidence = medium -explanation = In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor. -how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -annotations = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +explanation = This search finds systems that do not commonly communicate use remote desktop. It does this by filtering out all systems that have the "common_rdp_source" or "common_rdp_destination" category applied to that system. Categories are applied to systems using the Assets and Identity framework. +how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. +annotations = {"mitre_attack": ["Lateral Movement", "Remote Desktop Protocol", "Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +known_false_positives = Remote Desktop may be used legitimately by users on the network. +providing_technologies = ["Bro", "Splunk Stream"] -[savedsearch://ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule] +[savedsearch://ESCU - Detect new API calls from user roles - Rule] type = detection asset_type = AWS Instance confidence = medium -explanation = This search\ -\ -1. Retrieves the **AssumeRole** event\ -\ -1. Verifies that the log entry contains a value for the account ID of the requesting account\ -\ -1. Ensures that the requesting account ID does not match the account ID of the requested account\ -\ -1. Pulls in the previously seen requesting and requested account IDs\ -\ -1. Splits up and executes multiple search paths at the same.\ -\ -1. The first path determines the **firstTime** and **lastTime** entries for the cache file\ -\ -1. Outputs the data to the cache file.\ -\ -1. Creates a conditional statement that is always false (both because we don't want these values to exit the search pipeline and because we think we're clever).The second pipeline adds the **firstTime** and **lastTime** entries to search results. Next, it filters out any account pairs that haven't been seen for the first time within the last hour. The `isnotnull(_time)` will remove the entries from the cache file.\ -\ -The search finishes by gathering the data that it will display to the user. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search. -annotations = {"mitre_attack": ["Credential Access"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} -known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. +explanation = The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is `AssumedRole`. It then appends the historical data to those results in the lookup file. Next, it recalculates the `earliest` and `latest` fields for each user role, as well as the name of the API call, and returns only those roles and API calls that have first been seen in the past hour. This is combined with the main search to return the values of API calls, name of the user role, and the earliest and latest time of this activity. It is worth noting that the name of the role of a particular user is parsed as "userName" in the CloudTrail logs. +how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. providing_technologies = ["AWS"] +[savedsearch://ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of cmd.exe. +how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. +annotations = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] + + [savedsearch://ESCU - Remote Process Instantiation via WMI - Rule] type = detection asset_type = Endpoint @@ -3348,17 +3364,6 @@ known_false_positives = The wmic.exe utility is a benign Windows application. It providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Batch File Write to System32 - Rule] -type = detection -asset_type = Endpoint -confidence = high -explanation = This search looks at file modifications across your hosts, as well as for evidence of batch files being written to paths that include "system32." This activity is consistent with some SamSam attacks and is, in general, suspicious. -how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -annotations = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] - - [savedsearch://ESCU - Windows Updates Install Successes] type = support explanation = This search gives you the count and name of all the systems that had a successful update applied each day @@ -3425,36 +3430,31 @@ known_false_positives = Some legitimate applications start with long command-lin providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Baseline of blocked outbound traffic from AWS] -type = support -explanation = Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each source IP had. This table is then stored in a lookup file. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`. +[savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule] +type = detection +asset_type = Endpoint +confidence = high +explanation = This search looks at any emails with file attachment names that contain many spaces relative to the length of the file name. Specifically, it checks if spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will then output the message ID of the email, the count, the recipient address and the recipient user, first and last time this event was seen and the space ratio of the file attachment name. +how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. +annotations = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 7"], "nist": ["PR.IP"]} known_false_positives = None at this time -providing_technologies = ["AWS"] +providing_technologies = ["Microsoft Exchange"] -[savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] -type = detection -asset_type = AWS Instance -confidence = medium -explanation = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ -\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -providing_technologies = ["AWS"] +[savedsearch://ESCU - Count of Unique IPs Connecting to Ports] +type = support +explanation = For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections. +how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. +known_false_positives = None at this time +providing_technologies = ["Splunk Stream", "Bro"] -[savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] -type = detection -asset_type = AWS Instance -confidence = medium -explanation = The subsearch returns the instance types of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the earliest seen time field for each instance type and returns only those instance types that has first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. -how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. -annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. -providing_technologies = ["AWS"] +[savedsearch://ESCU - Identify Systems Receiving Remote Desktop Traffic] +type = support +explanation = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic. +how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it. +known_false_positives = None at this time +providing_technologies = ["Splunk Stream", "Bro"] [savedsearch://ESCU - Get Risk Modifiers For Endpoint] diff --git a/src/default/savedsearches.conf b/src/default/savedsearches.conf index 18fc6e1649..945fa054ab 100644 --- a/src/default/savedsearches.conf +++ b/src/default/savedsearches.conf @@ -47,145 +47,6 @@ schedule_window = auto is_visible = false search = ((sourcetype=*wineventlog:security) AND (EventCode=1102 OR EventCode=1100)) OR ((sourcetype=wineventlog:system OR sourcetype=XmlWinEventlog:System) AND EventCode=104) | stats count min(_time) as firstTime max(_time) as lastTime by EventCode sourcetype host | `ctime(firstTime)` | `ctime(lastTime)` | rename host as dest -[ESCU - Get Process Information For Port Activity] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-06-25 -action.escu.modification_date = 2017-09-10 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events. -action.escu.data_models = ["Application_State"] -action.escu.full_search_name = ESCU - Get Process Information For Port Activity -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.analytic_story = ["Ransomware", "Command and Control", "Use of Cleartext Protocols", "Prohibited Traffic Allowed or Protocol Mismatch", "SamSam Ransomware"] -action.escu.fields_required = ["dest_port", "src"] -action.escu.earliest_time_offset = 7200 -action.escu.latest_time_offset = 7200 -description = This search will return information about the process associated with observed network traffic to a specific destination port from a specific host. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | from datamodel Application_State.Ports | search dest_port=$dest_port$ dest=$src$ | table dest dest_port process process_name - -[ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-04-26 -action.escu.modification_date = 2018-05-07 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search retrieves all the VPC Flow log entries that have recorded a blocked outbound network connection originating from your AWS environment. Then it kicks off a subsearch, which looks at the same data and performs the following series of steps: \ -\ -1. Counts the number of blocked outbound connections by each source IP\ -\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the average blocked connections, and the standard deviation for each source IP.\ -\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -\ -1. Renames `numberOfBlockedConnections` as `latestCount`.\ -\ -1. Calculates the new average value for each source IP with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation, weighting the past more heavily than the current.\ -\ -1. Updates the cache file with the latest results.\ -\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -\ -1. Filters out anything that it determines is not a spike and returns the list of source IPs to the main search. The main search subsequently gets the list of all destination IPs for which the traffic was blocked, the network interface ID, the number of unique destination IP, and the total number of blocked connections for each of these source IP addresses. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. -action.escu.full_search_name = ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule -action.escu.mappings = {"mitre_attack": ["Exfiltration", "Command and Control"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "cis20": ["CIS 11"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} -action.escu.known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS Traffic", "Command and Control", "AWS Network ACL Activity"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in blocked Outbound Traffic from your AWS -action.notable = 1 -action.notable.param.nes_fields = src_ip -action.notable.param.rule_description = A spike in the blocked outbound connection is detected from source $src_ip$. -action.notable.param.rule_title = Spike in blocked outbound network connections from $src_ip$ detected. -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network ACL Details from ID\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip - -[ESCU - Reg.exe used to hide files/directories via registry keys - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-10-27 -action.escu.modification_date = 2017-10-30 -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = Reg.exe is a binary native to Windows platform used to edit the registry hives of the system. Attackers can leverage this binary to hide files by passing in arguments that are used to hide the files. In the search, we first gather results with keywords, add, Hidden, and REG_DWORD, that will be in the raw event and filter by process and the command-line. We then leverage regular expressions on the command-line field to look for /d value as 2 which is responsible for hiding a file or directory. -action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -action.escu.full_search_name = ESCU - Reg.exe used to hide files/directories via registry keys - Rule -action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -action.escu.known_false_positives = None at the moment -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics", "Windows Persistence Techniques"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Reg.exe used to hide files/directories via registry keys -action.notable = 1 -action.notable.param.nes_fields = dest, process, cmdline -action.notable.param.rule_description = Regedit.exe is used by attackers to hide malware files/directories in windows environments via registry key settings. This rule detects command-line arguments used to hide a file/directory -action.notable.param.rule_title = Regedit.exe used to hide a file/directory on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,cmdline -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = The search looks for command-line arguments used to hide a file or directory using the reg add command. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational add Hidden REG_DWORD | search process=*reg.exe cmdline=*add* cmdline=*Hidden* cmdline=*REG_DWORD* | regex cmdline= "(/d\s+2)" | stats count values(cmdline) min(_time) as firstTime max(_time) as lastTime by dest process | `ctime(firstTime)` |`ctime(lastTime)` - [ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] action.escu = 0 action.escu.enabled = 1 @@ -235,6 +96,128 @@ schedule_window = auto is_visible = false search = sourcetype="WinEventLog:Security" (EventCode=4624 OR EventCode=4625) Logon_Process=NtLmSsp Logon_Type=3 Account_Name !="ANONYMOUS LOGON" Key_Length=0 | table _time Source_Network_Address Account_Name Account_Domain ComputerName Workstation_Name +[ESCU - Create or delete hidden shares using net.exe - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-06-14 +action.escu.modification_date = 2018-11-15 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete hidden file shares by appending "$" after the name of the share. To look for hidden shares, use a regular expression to look for a `(name_file_share)$`. In this search, we are looking for the command-line execution of net.exe with command-line parameters such as `net`, `share`, or `delete` that may correspond to the creation of hidden shares +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Create or delete hidden shares using net.exe - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Hidden Cobra Malware"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Create or delete hidden shares using net.exe +action.notable = 1 +action.notable.param.nes_fields = dest,process_name +action.notable.param.rule_description = Net.exe was used to create or delete hidden network shares by $user$ on $dest$ +action.notable.param.rule_title = Hidden File shares created/deleted on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Outbound Emails to Hidden Cobra Threat Actors\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,process_name +alert.suppress.period = 86400s +cron_schedule = 5 * * * * +description = This search looks for the creation or deletion of hidden shares using net.exe. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search (process=*share* OR process=*delete*)| regex process="\S+[$]" + +[ESCU - Reg.exe used to hide files/directories via registry keys - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-10-27 +action.escu.modification_date = 2017-10-30 +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = Reg.exe is a binary native to Windows platform used to edit the registry hives of the system. Attackers can leverage this binary to hide files by passing in arguments that are used to hide the files. In the search, we first gather results with keywords, add, Hidden, and REG_DWORD, that will be in the raw event and filter by process and the command-line. We then leverage regular expressions on the command-line field to look for /d value as 2 which is responsible for hiding a file or directory. +action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +action.escu.full_search_name = ESCU - Reg.exe used to hide files/directories via registry keys - Rule +action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} +action.escu.known_false_positives = None at the moment +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Defense Evasion Tactics"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Reg.exe used to hide files/directories via registry keys +action.notable = 1 +action.notable.param.nes_fields = dest, process, cmdline +action.notable.param.rule_description = Regedit.exe is used by attackers to hide malware files/directories in windows environments via registry key settings. This rule detects command-line arguments used to hide a file/directory +action.notable.param.rule_title = Regedit.exe used to hide a file/directory on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,cmdline +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = The search looks for command-line arguments used to hide a file or directory using the reg add command. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational add Hidden REG_DWORD | search process=*reg.exe cmdline=*add* cmdline=*Hidden* cmdline=*REG_DWORD* | regex cmdline= "(/d\s+2)" | stats count values(cmdline) min(_time) as firstTime max(_time) as lastTime by dest process | `ctime(firstTime)` |`ctime(lastTime)` + +[ESCU - Get Process Information For Port Activity] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-06-25 +action.escu.modification_date = 2017-09-10 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that associates processes with network events. +action.escu.data_models = ["Application_State"] +action.escu.full_search_name = ESCU - Get Process Information For Port Activity +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +action.escu.analytic_story = ["Ransomware", "Use of Cleartext Protocols", "Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch", "SamSam Ransomware"] +action.escu.fields_required = ["dest_port", "src"] +action.escu.earliest_time_offset = 7200 +action.escu.latest_time_offset = 7200 +description = This search will return information about the process associated with observed network traffic to a specific destination port from a specific host. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | from datamodel Application_State.Ports | search dest_port=$dest_port$ dest=$src$ | table dest dest_port process process_name + [ESCU - TOR Traffic - Rule] action.escu = 0 action.escu.enabled = 1 @@ -301,7 +284,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], action.escu.known_false_positives = Some legitimate applications may exhibit this behavior. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Emotet Malware (TA18-201A)"] +action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "Suspicious Command-Line Executions"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detect Use of cmd.exe to Launch Script Interpreters action.notable = 1 @@ -310,7 +293,7 @@ action.notable.param.rule_description = Potentially malicious script execution d action.notable.param.rule_title = Command prompt is executing scripts on $dest$ using $process_name$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -359,55 +342,27 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` values(Web.url) as url from datamodel=Web by Web.src,Web.http_user_agent,Web.http_method | `drop_dm_object_name("Web")`| where like(src, "$src$") and like(http_method, "POST") -[ESCU - Detect Long DNS TXT Record Response - Rule] +[ESCU - Monitor Successful Backups] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-06-18 -action.escu.modification_date = 2017-09-18 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2017-08-24 +action.escu.modification_date = 2017-09-12 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search uses the Network_Resolution data model and gathers all the answers to DNS queries for TXT records. The query then looks at the answer section and calculates the length of the answer. The search will then return information for those responses that exceed 100 characters in length. -action.escu.how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. -action.escu.data_models = ["Network_Resolution"] -action.escu.full_search_name = ESCU - Detect Long DNS TXT Record Response - Rule -action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} -action.escu.known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. -action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Long DNS TXT Record Response -action.notable = 1 -action.notable.param.nes_fields = src, query -action.notable.param.rule_description = A DNS TXT record response of over 100 characters was detected. -action.notable.param.rule_title = Long DNS TXT Record Response -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. -dispatch.earliest_time = -70m@m +action.escu.eli5 = This search gives you the count and the hostname of all the systems that had a successful backup each day. +action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. +action.escu.full_search_name = ESCU - Monitor Successful Backups +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["Netbackup"] +action.escu.analytic_story = ["Ransomware", "Monitor Backup Solution", "SamSam Ransomware"] +description = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate. +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `ctime(firstTime)` | `ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" +search = sourcetype="netbackup_logs" "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE [ESCU - Get Logon Rights Modifications For Endpoint] action.escu = 0 @@ -454,45 +409,44 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail `securityGroupAPIs` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup security_group_activity_baseline | stats count -[ESCU - Create or delete hidden shares using net.exe - Rule] +[ESCU - Unsuccessful Netbackup backups - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-06-14 -action.escu.modification_date = 2018-11-15 +action.escu.creation_date = 2017-06-15 +action.escu.modification_date = 2017-09-12 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = Net.exe is a built-in command-line tool on Windows that can be used to create, delete, and manage shared resources on the computer, both locally and remotely. Though this tool is used by Microsoft administrators to manage the network shares, attackers also leverage it to create and delete hidden file shares by appending "$" after the name of the share. To look for hidden shares, use a regular expression to look for a `(name_file_share)$`. In this search, we are looking for the command-line execution of net.exe with command-line parameters such as `net`, `share`, or `delete` that may correspond to the creation of hidden shares -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Create or delete hidden shares using net.exe - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface", "Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.known_false_positives = Administrators often leverage net.exe to create or delete network shares. You should verify that the activity was intentional and is legitimate. +action.escu.confidence = high +action.escu.eli5 = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure. +action.escu.how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. +action.escu.full_search_name = ESCU - Unsuccessful Netbackup backups - Rule +action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} +action.escu.known_false_positives = None identified action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Hidden Cobra Malware"] +action.escu.providing_technologies = ["Netbackup"] +action.escu.analytic_story = ["Monitor Backup Solution"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Create or delete hidden shares using net.exe +action.correlationsearch.label = Unsuccessful Netbackup backups action.notable = 1 -action.notable.param.nes_fields = dest,process_name -action.notable.param.rule_description = Net.exe was used to create or delete hidden network shares by $user$ on $dest$ -action.notable.param.rule_title = Hidden File shares created/deleted on $dest$ +action.notable.param.nes_fields = dest +action.notable.param.rule_description = The system $dest$ attempted a backup but encountered an error. +action.notable.param.rule_title = Failed backup attempt by $dest$ action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Outbound Emails to Hidden Cobra Threat Actors\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - All backup logs for host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 +action.risk.param._risk_score = 10 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,process_name +alert.suppress.fields = dest alert.suppress.period = 86400s -cron_schedule = 5 * * * * -description = This search looks for the creation or deletion of hidden shares using net.exe. -dispatch.earliest_time = -70m@m +cron_schedule = 0 7 * * * +description = This search gives you the hosts where a backup was attempted and then failed. +dispatch.earliest_time = -24h@h dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -502,7 +456,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count values(Processes.user) as user values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processs.process_name=net.exe OR Processes.process_name=net1.exe) by Processes.process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search (process=*share* OR process=*delete*)| regex process="\S+[$]" +search = sourcetype="netbackup_logs" | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature [ESCU - Detect processes used for System Network Configuration Discovery - Rule] action.escu = 0 @@ -859,7 +813,7 @@ action.escu.full_search_name = ESCU - Get Registry Activities action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Suspicious MSHTA Activity"] +action.escu.analytic_story = ["Suspicious MSHTA Activity", "Suspicious Windows Registry Activities"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 86400 @@ -919,27 +873,54 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) EventCode=13 object_path=*\\Explorer\\FileExts* process!=Explorer.exe AND process!=OpenWith.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, process, object_path, Details | rename Details as value | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - Baseline of Network ACL Activity by ARN] +[ESCU - Detect S3 access from a new IP - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-05-21 -action.escu.modification_date = 2018-05-21 +action.escu.creation_date = 2018-06-25 +action.escu.modification_date = 2018-06-28 +action.escu.asset_at_risk = S3 Bucket action.escu.channel = ESCU -action.escu.eli5 = Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `NetworkACLEvents`. -action.escu.full_search_name = ESCU - Baseline of Network ACL Activity by ARN -action.escu.known_false_positives = None at this time -action.escu.search_type = support +action.escu.confidence = low +action.escu.eli5 = Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code "200") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. Next, it returns only those remote IP addresses that have first been seen accessing a specific bucket within the past hour. This is combined with the main search to return the time, bucket name, source IP, city, and country operations performed, as well as the requested URI of the resource +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. +action.escu.full_search_name = ESCU - Detect S3 access from a new IP - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13", "CIS 14"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour +action.escu.search_type = detection action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Network ACL Activity"] -description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. -dispatch.earliest_time = -30d@d +action.escu.analytic_story = ["Suspicious AWS S3 Activities"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect S3 access from a new IP +action.notable = 1 +action.notable.param.nes_fields = bucket_name, src_ip +action.notable.param.rule_description = A remote IP, $src_ip$, has made a successful connection with an S3 $bucket_name$. +action.notable.param.rule_title = S3 bucket $bucketName$ was accessed by a new $src_ip$ +action.notable.param.security_domain = network +action.notable.param.severity = low +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src_ip +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 20 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = bucket_name, src_ip +alert.suppress.period = 86400s +cron_schedule = 5 * * * * +description = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail `NetworkACLEvents` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats count +search = sourcetype=aws:s3:accesslogs http_status=200 [search sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | convert ctime(firstTime) ctime(lastTime) | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri [ESCU - Remote Desktop Process Running On System - Rule] action.escu = 0 @@ -991,56 +972,6 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=mstsc.exe AND Processes.dest_category!=common_rdp_source by Processes.dest Processes.user Processes.process | `ctime(firstTime)`| `ctime(lastTime)` | `drop_dm_object_name(Processes)` -[ESCU - Email files written outside of the Outlook directory - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-12-13 -action.escu.modification_date = 2018-11-02 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\users\username\My Documents\Outlook Files\. -action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Email files written outside of the Outlook directory - Rule -action.escu.mappings = {"mitre_attack": ["Collection", "Email Collection"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"]} -action.escu.known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Collection and Staging"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Email files written outside of the Outlook directory -action.notable = 1 -action.notable.param.nes_fields = dest, file_path, action, file_name -action.notable.param.rule_description = The system $dest$ has email files outside of the normal Outlook directory -action.notable.param.rule_title = Email files created or modified on $dest$ that are not in the normal Outlook directory -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, file_path -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.dll OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `ctime(firstTime)` | `ctime(lastTime)` - [ESCU - Detect New Local Admin account - Rule] action.escu = 0 action.escu.enabled = 1 @@ -1090,65 +1021,44 @@ schedule_window = auto is_visible = false search = sourcetype=wineventlog:security EventCode=4720 OR (EventCode=4732 Group_Name= Administrators) | transaction Security_ID maxspan=180m | search EventCode=4720 EventCode=4732 | table _time user dest EventCode Security_ID Group_Name src_user Message -[ESCU - Detect Spike in S3 Bucket deletion - Rule] +[ESCU - Remote Desktop Network Bruteforce - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-07-17 -action.escu.modification_date = 2018-11-27 -action.escu.asset_at_risk = S3 Bucket +action.escu.creation_date = 2018-12-14 +action.escu.modification_date = 2018-12-14 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = This search and its corresponding subsearch run through the following series of steps: \ -\ -1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for deletion of S3 buckets.\ -\ -1. Kick off a subsearch that retrieves the same data and pulls out and converts the ARN into a more friendly format.\ -\ -1. Count the number of API calls per ARN.\ -\ -1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -\ -1. Drop the count from the latest hour, since it is unnecessary, and merge the rest of the data with the results of the `stats` command. \ -\ -1. Rename `apiCalls` as `latestCount`.\ -\ -1. Calculate the new average value for each ARN with the latest count, weighting the past more heavily than the current hour. It does the same for the standard deviation—weighting the past more heavily than the current.\ -\ -1. Update the cache file with the latest results.\ -\ -1. Set the minimum threshold for the number of data points and the number of standard deviations away from the mean it must be to be considered a spike.\ -\ -1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and if the count is a sufficient number of standard deviations away from the average.\ -\ -1. Filter out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of the deleted S3 buckets, the number of unique API calls, and the total number of API calls for each of these user ARNs. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. -action.escu.full_search_name = ESCU - Detect Spike in S3 Bucket deletion - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. +action.escu.eli5 = This search monitors for abnormal amounts of remote-desktop (RDP) traffic from a source to a destination that may be indicative of a brute-force attack. It does this by filtering out RDP traffic from the Network_Traffic.All_Traffic data model, using twice the standard deviation of all source-to-destination connections. If any tuple is within more than two standard deviations of all other usual RDP traffic flows, it is indicative of a brute-force attack. +action.escu.how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Remote Desktop Network Bruteforce - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "cis20": ["CIS 12", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +action.escu.known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities"] +action.escu.providing_technologies = ["Bro", "Splunk Stream"] +action.escu.analytic_story = ["SamSam Ransomware"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in S3 Bucket deletion +action.correlationsearch.label = Remote Desktop Network Bruteforce action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A spike in the number of S3 buckets deleted by $user$ was detected. -action.notable.param.rule_title = Spike detected in S3 bucket deletion activity by $user$. +action.notable.param.nes_fields = dest, src +action.notable.param.rule_description = Remote-desktop traffic detected from $src$ to $dest$. This activity is consistent with a brute-force attack. +action.notable.param.rule_title = Bruteforce Remote Desktop Network Traffic detected from $src$ to $dest$ action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 +action.risk.param._risk_object = src +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 75 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 14400s +alert.suppress.fields = dest,src +alert.suppress.period = 28800s cron_schedule = 0 * * * * -description = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. +description = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -1159,7 +1069,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=DeleteBucket [search sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50| where count>(stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev [ESCU - Web Fraud - Anomalous User Clickspeed - Rule] action.escu = 0 @@ -1226,7 +1136,7 @@ action.escu.mappings = {"mitre_attack": ["Discovery", "System Information Discov action.escu.known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] -action.escu.analytic_story = ["SamSam Ransomware", "JBoss Vulnerability"] +action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detect attackers scanning for vulnerable JBoss servers action.notable = 1 @@ -1235,7 +1145,7 @@ action.notable.param.rule_description = This search looks for specific GET/HEAD action.notable.param.rule_title = Detect attackers scanning for vulnerable JBoss servers action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -1333,29 +1243,6 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:cloudtrail awsRegion=$awsRegion$| rename requestParameters.instancesSet.items{}.instanceId as instanceId| stats values(eventName) by userName instanceId -[ESCU - Get EC2 Instance Details by instanceId] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-02-12 -action.escu.modification_date = 2018-02-12 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. -action.escu.full_search_name = ESCU - Get EC2 Instance Details by instanceId -action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "Unusual AWS EC2 Modifications"] -action.escu.fields_required = ["instanceId"] -action.escu.earliest_time_offset = 86400 -action.escu.latest_time_offset = 0 -description = This search queries AWS description logs and returns all the information about a specific instance via the instanceId field -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | search sourcetype="aws:description" source="*:ec2_instances"| dedup id sortby -_time | search id=$instanceId$ | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC - [ESCU - AWS Network Access Control List Created with All Open Ports - Rule] action.escu = 0 action.escu.enabled = 1 @@ -1454,56 +1341,6 @@ schedule_window = auto is_visible = false search = sourcetype=wineventlog:security EventCode=4703 Process_Name=*powershell.exe | rex field=Message "Enabled Privileges:\s+(?\w+)\s+Disabled Privileges:" | where privs="SeDebugPrivilege" | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, privs, Process_ID, Message | rename privs as "Enabled Privilege" | rename Process_Name as process | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - Prohibited Network Traffic Allowed - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-04-18 -action.escu.modification_date = 2017-09-11 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = The search looks for traffic marked 'is_prohibited' in the Enterprise Security lookup table 'interesting_ports_lookup', and then determines if any network devices have an associated 'allow' action on that traffic by checking the Network_Traffic data model. -action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Prohibited Network Traffic Allowed - Rule -action.escu.mappings = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "kill_chain_phases": ["Delivery", "Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE", "PR.AC"]} -action.escu.known_false_positives = None identified -action.escu.search_type = detection -action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] -action.escu.analytic_story = ["Ransomware", "Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Prohibited Network Traffic Allowed -action.notable = 1 -action.notable.param.nes_fields = src_ip, dest_ip -action.notable.param.rule_description = This search looks for network traffic defined by port and transport in the ES lookup table "lookup_interesting_ports", that is marked as prohibited, and yet has an 'allow' action in the Network_Traffic data model. This should help to identify areas where a network device is not properly configured. -action.notable.param.rule_title = Prohibited Network Traffic Allowed from $src_ip$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Information For Port Activity\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest_ip,src_ip -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` - [ESCU - Detect Unauthorized Assets by MAC address - Rule] action.escu = 0 action.escu.enabled = 1 @@ -1875,43 +1712,44 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename userIdentity.arn as arn | stats earliest(_time) as firstTime latest(_time) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | stats count -[ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] +[ESCU - Detect Long DNS TXT Record Response - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-08-28 -action.escu.modification_date = 2018-08-28 -action.escu.asset_at_risk = Windows +action.escu.creation_date = 2017-06-18 +action.escu.modification_date = 2017-09-18 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = This search looks for Windows Event Code 4663 (object access), where the process performing the access is PowerShell.exe, the target process of the access is lsass.exe, and the access mask is given as 0x10. This is consistent with the use of PowerShell to execute Mimikatz using sekurlsa::logonpasswords. It will return the host where the activity occurred, the process and associated id, the enabled privilege, and the message in the event. -action.escu.how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. -action.escu.full_search_name = ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access", "Credential Dumping"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} -action.escu.known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. +action.escu.eli5 = This search uses the Network_Resolution data model and gathers all the answers to DNS queries for TXT records. The query then looks at the answer section and calculates the length of the answer. The search will then return information for those responses that exceed 100 characters in length. +action.escu.how_to_implement = To successfully implement this search you need to ingest data from your DNS logs, or monitor DNS traffic using Stream, Bro or something similar. Specifically, this query requires that the DNS data model is populated with information regarding the DNS record type that is being returned as well as the data in the answer section of the protocol. +action.escu.data_models = ["Network_Resolution"] +action.escu.full_search_name = ESCU - Detect Long DNS TXT Record Response - Rule +action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 8", "CIS 12", "CIS 13"], "nist": ["PR.DS", "PR.PT", "DE.AE", "DE.CM"]} +action.escu.known_false_positives = It's possible that legitimate TXT record responses can be long enough to trigger this search. You can modify the packet threshold for this search to help mitigate false positives. action.escu.search_type = detection -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Credential Dumping"] +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Mimikatz Via PowerShell And EventCode 4663 +action.correlationsearch.label = Detect Long DNS TXT Record Response action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = Possible attempt at credential dumping via PowerShell was detected on $dest$ by $user$. -action.notable.param.rule_title = Event ID 4663 Specifying PowerShell Reading From LSASS.exe Identified on $dest$. -action.notable.param.security_domain = access +action.notable.param.nes_fields = src, query +action.notable.param.rule_description = A DNS TXT record response of over 100 characters was detected. +action.notable.param.rule_title = Long DNS TXT Record Response +action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest +action.risk.param._risk_object = src action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 +action.risk.param._risk_score = 70 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = user, dest, process +alert.suppress.fields = src alert.suppress.period = 86400s cron_schedule = 0 * * * * -description = This search looks for PowerShell reading lsass memory consistent with credential dumping. +description = This search is used to detect attempts to use DNS tunneling, by calculating the length of responses to DNS TXT queries. Endpoints using DNS as a method of transmission for data exfiltration, command and control, or evasion of security controls can often be detected by noting unusually large volumes of DNS traffic. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -1922,7 +1760,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=wineventlog:security EventCode=4663 Process_Name=*powershell.exe Object_Name=*lsass.exe Access_Mask=0x10 | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, Process_ID, Message | rename Process_Name as process | `ctime(firstTime)`| `ctime(lastTime)` +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.message_type=response AND DNS.record_type=TXT by DNS.src DNS.dest DNS.answer DNS.record_type | `drop_dm_object_name("DNS")` | eval anslen=len(answer) | search anslen>100 | `ctime(firstTime)` | `ctime(lastTime)` | rename src as "Source IP", dest as "Destination IP", answer as "DNS Answer" anslen as "Answer Length" record_type as "DNS Record Type" firstTime as "First Time" lastTime as "Last Time" count as Count | table "Source IP" "Destination IP" "DNS Answer" "DNS Record Type" "Answer Length" Count "First Time" "Last Time" [ESCU - Suspicious Reg.exe Process - Rule] action.escu = 0 @@ -1939,7 +1777,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Modify Registry", " action.escu.known_false_positives = It's possible for system administrators to write scripts that exhibit this behavior. If this is the case, the search will need to be modified to filter them out. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Windows Defense Evasion Tactics", "Disabling Security Tools"] +action.escu.analytic_story = ["DHS Report TA18-074A", "Disabling Security Tools", "Windows Defense Evasion Tactics"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Suspicious Reg.exe Process action.notable = 1 @@ -1948,7 +1786,7 @@ action.notable.param.rule_description = The system $dest$ had reg.exe process ru action.notable.param.rule_title = Suspicious reg.exe process detected on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -2045,28 +1883,54 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId!=requestedAccountId | stats earliest(_time) as firstTime latest(_time) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | stats count -[ESCU - Count of assets by category] +[ESCU - Suspicious writes to windows Recycle Bin - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-06-11 -action.escu.modification_date = 2017-09-13 +action.escu.creation_date = 2018-01-08 +action.escu.modification_date = 2018-01-08 +action.escu.asset_at_risk = Windows action.escu.channel = ESCU -action.escu.eli5 = This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count. -action.escu.how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated. -action.escu.data_models = ["Identity_Management"] -action.escu.full_search_name = ESCU - Count of assets by category -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Asset Tracking"] -description = This search shows you every asset category you have and the assets that belong to those categories. -dispatch.earliest_time = -30d@d +action.escu.confidence = medium +action.escu.eli5 = This search uses data on file writes captured via Sysmon to watch for writes to the Recycle Bin by processes other than explorer.exe. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. Next, it looks for files created with a path that includes the string "$Recycle.Bin" by processes other than explorer.exe, which is the process responsible for copying files to the Recycle Bin on delete. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. +action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +action.escu.full_search_name = ESCU - Suspicious writes to windows Recycle Bin - Rule +action.escu.mappings = {"mitre_attack": ["Collection", "Data Staged"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} +action.escu.known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. +action.escu.search_type = detection +action.escu.providing_technologies = ["Sysmon"] +action.escu.analytic_story = ["Collection and Staging"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Suspicious writes to windows Recycle Bin +action.notable = 1 +action.notable.param.nes_fields = dest, file_name, process +action.notable.param.rule_description = The process $process$ on $dest$ wrote $file_name$ to the Recycle Bin. +action.notable.param.rule_title = Suspicious process $process$ wrote to the Recycle Bin on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 70 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search detects writes to the recycle bin by a process other than explorer.exe. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | from datamodel Identity_Management.All_Assets | stats count values(nt_host) by category | sort -count +search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) EventCode=11 file_path=*$Recycle.Bin* process!=explorer.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path | `ctime(firstTime)`| `ctime(lastTime)` [ESCU - Suspicious File Write - Rule] action.escu = 0 @@ -2206,7 +2070,7 @@ action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", action.escu.known_false_positives = It's possible that an enterprise has more than five DNS servers that are configured in a round-robin rotation. Please customize the search, as appropriate. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Clients Connecting to Multiple DNS Servers action.notable = 1 @@ -2215,7 +2079,7 @@ action.notable.param.rule_description = This search allows you to identify the e action.notable.param.rule_title = Client $src$ Connecting to Multiple DNS Servers action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -2240,29 +2104,6 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count, values(DNS.dest) AS dest dc(DNS.dest) as dest_count from datamodel=Network_Resolution where DNS.message_type=QUERY by DNS.src | `drop_dm_object_name("Network_Resolution")` |where dest_count > 5 -[ESCU - Get Sysmon WMI Activity for Host] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-10-23 -action.escu.modification_date = 2018-10-23 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. -action.escu.full_search_name = ESCU - Get Sysmon WMI Activity for Host -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Sysmon"] -action.escu.analytic_story = ["Suspicious WMI Use"] -action.escu.fields_required = ["process", "dest"] -action.escu.earliest_time_offset = 7200 -action.escu.latest_time_offset = 7200 -description = This search queries Sysmon WMI events for the host of interest. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 EventCode<22 host=$dest$ | rename host as dest | table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter - [ESCU - Monitor Registry Keys for Print Monitors - Rule] action.escu = 0 action.escu.enabled = 1 @@ -2313,6 +2154,29 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.action=modified AND Registry.registry_path="*CurrentControlSet\\Control\\Print\\Monitors*" by Registry.dest, Registry.registry_key_name Registry.status Registry.user Registry.registry_path Registry.action | `drop_dm_object_name(Registry)` +[ESCU - Get Sysmon WMI Activity for Host] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-10-23 +action.escu.modification_date = 2018-10-23 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search, you must be collecting Sysmon data using Sysmon version 6.1 or greater and have Sysmon configured to generate events for WMI activity. In addition, you must have at least version 6.0.4 of the Sysmon TA installed to properly parse the fields. +action.escu.full_search_name = ESCU - Get Sysmon WMI Activity for Host +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Sysmon"] +action.escu.analytic_story = ["Suspicious WMI Use"] +action.escu.fields_required = ["process", "dest"] +action.escu.earliest_time_offset = 7200 +action.escu.latest_time_offset = 7200 +description = This search queries Sysmon WMI events for the host of interest. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" EventCode>18 EventCode<22 host=$dest$ | rename host as dest | table _time, dest, user, Name, Operation, EventType, Type, Query, Consumer, Filter + [ESCU - Identify Systems Creating Remote Desktop Traffic] action.escu = 0 action.escu.enabled = 1 @@ -2336,28 +2200,28 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count -[ESCU - Get User Information from Identity Table] +[ESCU - Get All AWS Activity From Region] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-04-10 -action.escu.modification_date = 2017-09-20 +action.escu.creation_date = 2018-03-19 +action.escu.modification_date = 2018-03-19 action.escu.channel = ESCU action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must have populated the identity table with information about your users. -action.escu.full_search_name = ESCU - Get User Information from Identity Table +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.full_search_name = ESCU - Get All AWS Activity From Region action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Apache Struts Vulnerability", "Credential Dumping", "Suspicious Windows Registry Activities", "DHS Report TA18-074A", "Suspicious Command-Line Executions", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Host Redirection", "Collection and Staging", "Ransomware", "Windows Service Abuse", "Command and Control", "ColdRoot MacOS RAT", "Orangeworm Attack Group", "SQL Injection", "Emotet Malware (TA18-201A)", "Disabling Security Tools", "Use of Cleartext Protocols", "Suspicious WMI Use", "Hidden Cobra Malware", "Unusual Processes", "Data Protection", "Suspicious AWS S3 Activities", "Suspicious AWS EC2 Activities", "Brand Monitoring", "Asset Tracking", "Account Monitoring and Controls", "Windows Privilege Escalation", "ColdRoot MacOS RAT", "SamSam Ransomware", "Monitor for Unauthorized Software", "Windows Persistence Techniques", "Malicious PowerShell", "Spectre And Meltdown Vulnerabilities", "Dynamic DNS", "Lateral Movement", "Suspicious DNS Traffic", "Suspicious AWS Login Activities", "Splunk Enterprise Vulnerability", "Netsh Abuse", "Windows Log Manipulation", "AWS Network ACL Activity", "Router & Infrastructure Security", "Suspicious Emails", "Suspicious MSHTA Activity", "JBoss Vulnerability", "Monitor for Updates", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] -action.escu.fields_required = ["user"] -action.escu.earliest_time_offset = 864000 -action.escu.latest_time_offset = 86400 -description = Gather more information about the user identified in the Notable Event. +action.escu.search_type = investigative +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] +action.escu.fields_required = ["Region"] +action.escu.earliest_time_offset = 14400 +action.escu.latest_time_offset = 0 +description = This search retrieves all the activity from a specific geographic region and will create a table containing the time, geographic region, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = | `identities` | search identity=$user$ | table _time, identity, first, last, email, category, watchlist +search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Region=$Region$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip, awsRegion, eventName, errorCode [ESCU - Get DNS Server History for a host] action.escu = 0 @@ -2371,7 +2235,7 @@ action.escu.full_search_name = ESCU - Get DNS Server History for a host action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Host Redirection", "Command and Control", "Data Protection", "Brand Monitoring", "Dynamic DNS", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Data Protection", "Suspicious DNS Traffic", "Dynamic DNS", "Brand Monitoring", "Command and Control", "Host Redirection"] action.escu.fields_required = ["src_ip"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 86400 @@ -2470,7 +2334,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Persistence", "Late action.escu.known_false_positives = This technique may be legitimately used by administrators to modify remote registries, so it's important to filter these events out. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics", "Windows Persistence Techniques", "Lateral Movement"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Windows Defense Evasion Tactics", "Lateral Movement"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Remote Registry Key modifications action.notable = 1 @@ -2504,45 +2368,44 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="\\\\*" by Registry.dest , Registry.status, Registry.user | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(Registry)` -[ESCU - Detect malicious requests to exploit JBoss servers - Rule] +[ESCU - Abnormally High AWS Instances Launched by User - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-10-04 -action.escu.modification_date = 2017-09-23 -action.escu.asset_at_risk = Web Server +action.escu.creation_date = 2018-02-26 +action.escu.modification_date = 2018-02-26 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. -action.escu.how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model -action.escu.data_models = ["Web"] -action.escu.full_search_name = ESCU - Detect malicious requests to exploit JBoss servers - Rule -action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 12", "CIS 4", "CIS 18"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} -action.escu.known_false_positives = No known false positives for this detection. +action.escu.confidence = medium +action.escu.eli5 = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a `threshold_value` in the search. Start with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. For your reference, we then keep only the outliers and calculate the number of standard deviations away the value is from the average. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. +action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by User - Rule +action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} +action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] -action.escu.analytic_story = ["SamSam Ransomware", "JBoss Vulnerability"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect malicious requests to exploit JBoss servers +action.correlationsearch.label = Abnormally High AWS Instances Launched by User action.notable = 1 -action.notable.param.nes_fields = src, dest_ip -action.notable.param.rule_description = A search for detecting malicious requests made to exploit jmx-console in JBoss servers. The bad requests have a long url length since it serves the payload via the url -action.notable.param.rule_title = Detected malicious requests to exploit JBoss servers +action.notable.param.nes_fields = userName +action.notable.param.rule_description = An abnormally high number of instances were launched by a user within in a 10-minute window +action.notable.param.rule_title = High Number of instances launched by $userName$ action.notable.param.security_domain = network -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 +action.risk.param._risk_object = userName +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,url,src -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. -dispatch.earliest_time = -70m@m +alert.suppress.fields = userName +alert.suppress.period = 3600s +cron_schedule = */10 * * * * +description = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -2552,7 +2415,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime +search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev [ESCU - Get Notable Info] action.escu = 0 @@ -2566,7 +2429,7 @@ action.escu.full_search_name = ESCU - Get Notable Info action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Apache Struts Vulnerability", "Credential Dumping", "DHS Report TA18-074A", "Suspicious Command-Line Executions", "Windows Defense Evasion Tactics", "Suspicious AWS Traffic", "Windows File Extension and Association Abuse", "Host Redirection", "Collection and Staging", "Windows Service Abuse", "Command and Control", "Splunk Enterprise Vulnerability CVE-2018-11409", "Orangeworm Attack Group", "Web Fraud Detection", "SQL Injection", "Emotet Malware (TA18-201A)", "Disabling Security Tools", "Use of Cleartext Protocols", "Suspicious WMI Use", "AWS User Monitoring", "Hidden Cobra Malware", "Data Protection", "Suspicious AWS S3 Activities", "Suspicious AWS EC2 Activities", "Brand Monitoring", "Asset Tracking", "Account Monitoring and Controls", "Windows Privilege Escalation", "Windows Persistence Techniques", "Malicious PowerShell", "DNS Amplification Attacks", "Spectre And Meltdown Vulnerabilities", "Dynamic DNS", "Lateral Movement", "Suspicious DNS Traffic", "Suspicious AWS Login Activities", "Splunk Enterprise Vulnerability", "Windows Log Manipulation", "AWS Network ACL Activity", "Router & Infrastructure Security", "JBoss Vulnerability", "Monitor for Updates", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Suspicious WMI Use", "JBoss Vulnerability", "Data Protection", "Web Fraud Detection", "AWS Network ACL Activity", "Hidden Cobra Malware", "Windows Log Manipulation", "Splunk Enterprise Vulnerability", "Windows File Extension and Association Abuse", "Suspicious DNS Traffic", "Windows Persistence Techniques", "Dynamic DNS", "AWS User Monitoring", "Suspicious AWS S3 Activities", "Apache Struts Vulnerability", "Credential Dumping", "Suspicious AWS Traffic", "Monitor for Updates", "Spectre And Meltdown Vulnerabilities", "Collection and Staging", "Emotet Malware (TA18-201A)", "DNS Amplification Attacks", "SQL Injection", "Windows Service Abuse", "Suspicious Command-Line Executions", "Windows Privilege Escalation", "Asset Tracking", "DHS Report TA18-074A", "Account Monitoring and Controls", "Malicious PowerShell", "Orangeworm Attack Group", "Router & Infrastructure Security", "Brand Monitoring", "Use of Cleartext Protocols", "Disabling Security Tools", "Command and Control", "Suspicious AWS EC2 Activities", "Splunk Enterprise Vulnerability CVE-2018-11409", "Host Redirection", "Windows Defense Evasion Tactics", "Lateral Movement", "Suspicious AWS Login Activities", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] action.escu.fields_required = ["event_id"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 3600 @@ -2593,7 +2456,7 @@ action.escu.mappings = {"mitre_attack": ["Exfiltration", "Exfiltration Over Alte action.escu.known_false_positives = It is possible legitimate traffic can trigger this rule. Please investigate as appropriate. The threshold for generating an event can also be customized to better suit your environment. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Excessive DNS Failures action.notable = 1 @@ -2602,7 +2465,7 @@ action.notable.param.rule_description = This search identifies DNS query failure action.notable.param.rule_title = Excessive DNS Failures action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -2643,7 +2506,7 @@ action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", action.escu.known_false_positives = It's possible there can be long domain names that are legitimate. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Hidden Cobra Malware", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious DNS Traffic", "Command and Control"] action.correlationsearch.enabled = 1 action.correlationsearch.label = DNS Query Length With High Standard Deviation action.notable = 1 @@ -2652,7 +2515,7 @@ action.notable.param.rule_description = Filter DNS requests and compute the stan action.notable.param.rule_title = DNS query length with high standard deviation action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -2727,44 +2590,87 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=fsutil.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search process="*deletejournal*" AND process="*usn*" -[ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] +[ESCU - Previously Seen AWS Regions] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-10-07 -action.escu.modification_date = 2018-11-15 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-01-08 +action.escu.modification_date = 2018-01-08 +action.escu.channel = ESCU +action.escu.eli5 = In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value `awsRegion`. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_aws_regions.csv` lookup file, which will act like a baseline for detections. Please validate the entries of region names in the lookup file. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +action.escu.full_search_name = ESCU - Previously Seen AWS Regions +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] +description = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days +dispatch.earliest_time = -30d@d +dispatch.latest_time = -10m@m +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count + +[ESCU - Detect Spike in AWS API Activity - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-03-12 +action.escu.modification_date = 2018-04-09 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of cmd.exe. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. +action.escu.eli5 = This search and its corresponding subsearch run through a series of steps, as per the following: \ +\ +1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls.\ +\ +1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ +\ +1. Counts the number of API calls per ARN.\ +\ +1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ +\ +1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ +\ +1. Renames `apiCalls` as `latestCount`.\ +\ +1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ +\ +1. Updates the cache file with the latest results.\ +\ +1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ +\ +1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ +\ +1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. +action.escu.full_search_name = ESCU - Detect Spike in AWS API Activity - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.known_false_positives = action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS User Monitoring"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Prohibited Applications Spawning cmd.exe +action.correlationsearch.label = Detect Spike in AWS API Activity action.notable = 1 -action.notable.param.nes_fields = dest, process, parent_process -action.notable.param.rule_description = A prohibited application from prohibited_apps_launching_cmd.csv was leveraged to launch cmd.exe -action.notable.param.rule_title = Prohibited application($parent_process$) used to launch cmd.exe on $dest$ -action.notable.param.security_domain = endpoint +action.notable.param.nes_fields = user +action.notable.param.rule_description = A spike in the number of AWS API calls by $user$ was detected. +action.notable.param.rule_title = Spike in AWS API activity detected by $user$ +action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Registry Activities\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Investigate AWS User Activities by user field\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 +action.risk.param._risk_object = user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest, parent_process -alert.suppress.period = 86400s +alert.suppress.fields = user +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. +description = This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -2775,7 +2681,56 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count values(Processes.user) as user values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] +search = sourcetype=aws:cloudtrail eventType=AwsApiCall [search sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user + +[ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-08-28 +action.escu.modification_date = 2018-08-28 +action.escu.asset_at_risk = Windows +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = This search looks for Windows Event Code 4663 (object access), where the process performing the access is PowerShell.exe, the target process of the access is lsass.exe, and the access mask is given as 0x10. This is consistent with the use of PowerShell to execute Mimikatz using sekurlsa::logonpasswords. It will return the host where the activity occurred, the process and associated id, the enabled privilege, and the message in the event. +action.escu.how_to_implement = You must be ingesting Windows Security logs. You must also enable the account change auditing here: http://docs.splunk.com/Documentation/Splunk/7.0.2/Data/MonitorWindowseventlogdata. Additionally, this search requires you to enable your Group Management Audit Logs in your Local Windows Security Policy and to be ingesting those logs. More information on how to enable them can be found here: http://whatevernetworks.com/auditing-group-membership-changes-in-active-directory/. Finally, please make sure that the local administrator group name is "Administrators" to be able to look for the right group membership changes. +action.escu.full_search_name = ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access", "Credential Dumping"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 16"], "nist": ["PR.IP", "PR.AC", "DE.CM"]} +action.escu.known_false_positives = The activity may be legitimate. PowerShell is often used by administrators to perform various tasks, and it's possible this event could be generated in those cases. In these cases, false positives should be fairly obvious and you may need to tweak the search to eliminate noise. +action.escu.search_type = detection +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Credential Dumping"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Mimikatz Via PowerShell And EventCode 4663 +action.notable = 1 +action.notable.param.nes_fields = user, dest +action.notable.param.rule_description = Possible attempt at credential dumping via PowerShell was detected on $dest$ by $user$. +action.notable.param.rule_title = Event ID 4663 Specifying PowerShell Reading From LSASS.exe Identified on $dest$. +action.notable.param.security_domain = access +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 40 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = user, dest, process +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for PowerShell reading lsass memory consistent with credential dumping. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=wineventlog:security EventCode=4663 Process_Name=*powershell.exe Object_Name=*lsass.exe Access_Mask=0x10 | stats count min(_time) as firstTime max(_time) as lastTime by dest, Process_Name, Process_ID, Message | rename Process_Name as process | `ctime(firstTime)`| `ctime(lastTime)` [ESCU - WMI Temporary Event Subscription - Rule] action.escu = 0 @@ -2991,7 +2946,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Masquerading"], "ki action.escu.known_false_positives = None identified action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Ransomware", "Unusual Processes"] +action.escu.analytic_story = ["Ransomware", "Suspicious Command-Line Executions", "Unusual Processes"] action.correlationsearch.enabled = 1 action.correlationsearch.label = System Processes Run From Unexpected Locations action.notable = 1 @@ -3025,43 +2980,44 @@ schedule_window = auto is_visible = false search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational EventCode=1 NOT Image="C:\\Windows\\System32*" NOT Image="C:\\Windows\\SysWOW64*" | rex field=Image .*\\\(?\S+)\s?$ | `isWindowsSystemFile` | rename Image as process | table _time, dest, user, process, process_id, parent_process -[ESCU - Detect new user AWS Console Login - Rule] +[ESCU - Attempt To Add Certificate To Untrusted Store - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-02-26 -action.escu.modification_date = 2018-02-26 -action.escu.asset_at_risk = AWS Instance +action.escu.creation_date = 2018-04-09 +action.escu.modification_date = 2018-11-15 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days -action.escu.full_search_name = ESCU - Detect new user AWS Console Login - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.AE"]} -action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. +action.escu.confidence = high +action.escu.eli5 = Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of **certutil.exe** with the parameters `-addcert` and `disallowed`, which add a certification to the "untrusted" certificate store. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Attempt To Add Certificate To Untrusted Store - Rule +action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS Login Activities"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Disabling Security Tools"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect new user AWS Console Login +action.correlationsearch.label = Attempt To Add Certificate To Untrusted Store action.notable = 1 -action.notable.param.nes_fields = arn -action.notable.param.rule_description = A new user has logged into the AWS console -action.notable.param.rule_title = AWS Console Login by New User -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n"} +action.notable.param.nes_fields = dest, user, process_name +action.notable.param.rule_description = Attempt to add a certificate to the untrusted certificate store +action.notable.param.rule_title = Attempt To Add Certificate to Untrusted Store +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = arn -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = arn +alert.suppress.fields = process, dest alert.suppress.period = 86400s -cron_schedule = 5 * * * * -description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour +cron_schedule = 0 * * * * +description = Attempt to add a certificate to the untrusted certificate store dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -3072,7 +3028,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as arn |stats earliest(_time) as earliest latest(_time) as latest by arn | inputlookup append=t previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by arn | outputlookup previously_seen_users_console_logins.csv | eval userStatus=if(earliest >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | convert ctime(earliest) ctime(latest) | where userStatus ="First Time Logging into AWS Console" +search = | tstats `summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe (Processes.process=*-addstore* AND Processes.process=*disallowed* ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `ctime(firstTime)`|`ctime(lastTime)` [ESCU - Previously seen S3 bucket access by remote IP] action.escu = 0 @@ -3146,55 +3102,28 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)`|`ransomware_notes` -[ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] +[ESCU - Get EC2 Instance Details by instanceId] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-09-18 -action.escu.modification_date = 2018-12-03 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-02-12 +action.escu.modification_date = 2018-02-12 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search looks for PowerShell processes that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` flag looks to see it the default execution policy of PowerShell is being overridden, while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Malicious PowerShell"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments -action.notable = 1 -action.notable.param.nes_fields = dest, user, process, process_name -action.notable.param.rule_description = The system $dest$ executed a PowerShell that had an encoded command on the command-line, attempted to bypass local execution policy, and prevented the display of an interactive prompt to the user. -action.notable.param.rule_title = PowerShell process with multiple suspicious command-line arguments detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, process_name -alert.suppress.period = 14400s -cron_schedule = 50 * * * * -description = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.eli5 = none +action.escu.how_to_implement = In order to implement this search, you must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS(version 4.4.0 or later) and configure your AWS description inputs. +action.escu.full_search_name = ESCU - Get EC2 Instance Details by instanceId +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Unusual AWS EC2 Modifications", "Suspicious AWS EC2 Activities"] +action.escu.fields_required = ["instanceId"] +action.escu.earliest_time_offset = 86400 +action.escu.latest_time_offset = 0 +description = This search queries AWS description logs and returns all the information about a specific instance via the instanceId field disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* AND process=*-NonI* +search = | search sourcetype="aws:description" source="*:ec2_instances"| dedup id sortby -_time | search id=$instanceId$ | spath output=tags path=tags | eval tags=mvzip(key,value," = "), ip_address=if((ip_address == "null"),private_ip_address,ip_address) | table id, tags.Name, aws_account_id, placement, instance_type, key_name, ip_address, launch_time, state, vpc_id, subnet_id, tags | rename aws_account_id as "Account ID", id as ID, instance_type as Type, ip_address as "IP Address", key_name as "Key Pair", launch_time as "Launch Time", placement as "Availability Zone", state as State, subnet_id as Subnet, "tags.Name" as Name, vpc_id as VPC [ESCU - Malicious PowerShell Process - Execution Policy Bypass - Rule] action.escu = 0 @@ -3295,28 +3224,6 @@ schedule_window = auto is_visible = false search = index=_internal sourcetype=splunkd_ui_access server-info | search clientip!=127.0.0.1 uri_path="*raw/services/server/info/server-info" | rename clientip as src_ip, splunk_server as dest | stats earliest(_time) as firstTime, latest(_time) as lastTime, values(uri) as uri, values(useragent) as http_user_agent, values(user) as user by src_ip, dest | convert ctime(firstTime) ctime(lastTime) -[ESCU - Monitor Successful Backups] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-08-24 -action.escu.modification_date = 2017-09-12 -action.escu.channel = ESCU -action.escu.eli5 = This search gives you the count and the hostname of all the systems that had a successful backup each day. -action.escu.how_to_implement = To successfully implement this search you must be ingesting your backup logs. -action.escu.full_search_name = ESCU - Monitor Successful Backups -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["Ransomware", "SamSam Ransomware", "Monitor Backup Solution"] -description = This search is intended to give you a feel for how often successful backups are conducted in your environment. Fluctuations in these numbers will allow you to determine when you should investigate. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype="netbackup_logs" "Disk/Partition backup completed successfully." | bucket _time span=1d | stats dc(COMPUTERNAME) as count values(COMPUTERNAME) as dest by _time, MESSAGE - [ESCU - Get Emails From Specific Sender] action.escu = 0 action.escu.enabled = 1 @@ -3330,7 +3237,7 @@ action.escu.full_search_name = ESCU - Get Emails From Specific Sender action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.analytic_story = ["Web Fraud Detection", "Brand Monitoring", "Suspicious Emails"] +action.escu.analytic_story = ["Web Fraud Detection", "Suspicious Emails", "Brand Monitoring"] action.escu.fields_required = ["src_user"] action.escu.earliest_time_offset = 86400 action.escu.latest_time_offset = 86400 @@ -3479,7 +3386,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "Scripting", "Persistence" action.escu.known_false_positives = Legitimate programs can also use command-line arguments to execute. Please verify the command-line arguments to check what command/program is being executed. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Hidden Cobra Malware", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious Command-Line Executions", "DHS Report TA18-074A", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] action.correlationsearch.enabled = 1 action.correlationsearch.label = First time seen command line argument action.notable = 1 @@ -3563,6 +3470,56 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Email")` | `suspicious_email_attachments` +[ESCU - Windows hosts file modification - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-06-07 +action.escu.modification_date = 2018-11-02 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = high +action.escu.eli5 = The hosts file is present on both Windows and Linux endpoints. The purpose of the hosts file is to provide a mapping between hostnames and IP addresses, the same way DNS is used to provide such a mapping. However, the information in the hosts file takes precedence over information received via DNS and a DNS query will not be issued if the hostname of interest is found in the hosts file. As such, attackers have been observed adding entries to the host file to override any DNS resolution. For this reason, it is useful to monitor for changes to this file, which typically do not occur very often in legitimate cases. +action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Windows hosts file modification - Rule +action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 3", "CIS 8", "CIS 12"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} +action.escu.known_false_positives = There may be legitimate reasons for system administrators to add entries to this file. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.analytic_story = ["Host Redirection"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Windows hosts file modification +action.notable = 1 +action.notable.param.nes_fields = dest, file_name +action.notable.param.rule_description = A file modification was noted for the hosts file on $dest$. +action.notable.param.rule_title = Modification of hosts file detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get Process responsible for the DNS traffic\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 80 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,user +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = The search looks for modifications to the hosts file on all Windows endpoints across your environment. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest | `ctime(lastTime)` | `ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` + [ESCU - Get Notable History] action.escu = 0 action.escu.enabled = 1 @@ -3575,7 +3532,7 @@ action.escu.full_search_name = ESCU - Get Notable History action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Apache Struts Vulnerability", "Credential Dumping", "Suspicious Windows Registry Activities", "DHS Report TA18-074A", "Suspicious Command-Line Executions", "Windows Defense Evasion Tactics", "Suspicious AWS Traffic", "Windows File Extension and Association Abuse", "Host Redirection", "Collection and Staging", "Ransomware", "Windows Service Abuse", "Command and Control", "Splunk Enterprise Vulnerability CVE-2018-11409", "ColdRoot MacOS RAT", "Orangeworm Attack Group", "Web Fraud Detection", "SQL Injection", "Emotet Malware (TA18-201A)", "Disabling Security Tools", "Use of Cleartext Protocols", "Suspicious WMI Use", "AWS User Monitoring", "Prohibited Traffic Allowed or Protocol Mismatch", "Hidden Cobra Malware", "Unusual Processes", "Data Protection", "Suspicious AWS S3 Activities", "Suspicious AWS EC2 Activities", "Brand Monitoring", "Asset Tracking", "Account Monitoring and Controls", "Unusual AWS EC2 Modifications", "AWS Cross Account Activity", "Windows Privilege Escalation", "ColdRoot MacOS RAT", "SamSam Ransomware", "Monitor for Unauthorized Software", "Windows Persistence Techniques", "Malicious PowerShell", "DNS Amplification Attacks", "Spectre And Meltdown Vulnerabilities", "Dynamic DNS", "Lateral Movement", "Suspicious DNS Traffic", "Suspicious AWS Login Activities", "Splunk Enterprise Vulnerability", "Netsh Abuse", "Windows Log Manipulation", "AWS Network ACL Activity", "Monitor Backup Solution", "Router & Infrastructure Security", "Suspicious Emails", "Suspicious MSHTA Activity", "JBoss Vulnerability", "Monitor for Updates", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Suspicious WMI Use", "Unusual AWS EC2 Modifications", "ColdRoot MacOS RAT", "JBoss Vulnerability", "Data Protection", "Web Fraud Detection", "Ransomware", "Suspicious MSHTA Activity", "AWS Network ACL Activity", "Hidden Cobra Malware", "Windows Log Manipulation", "Suspicious Windows Registry Activities", "Splunk Enterprise Vulnerability", "Windows File Extension and Association Abuse", "Suspicious DNS Traffic", "Windows Persistence Techniques", "Dynamic DNS", "AWS User Monitoring", "AWS Cross Account Activity", "Suspicious AWS S3 Activities", "Apache Struts Vulnerability", "Credential Dumping", "Suspicious AWS Traffic", "Monitor for Updates", "Spectre And Meltdown Vulnerabilities", "Collection and Staging", "Emotet Malware (TA18-201A)", "Monitor for Unauthorized Software", "DNS Amplification Attacks", "Suspicious Emails", "SQL Injection", "Monitor Backup Solution", "Windows Service Abuse", "Suspicious Command-Line Executions", "Windows Privilege Escalation", "Asset Tracking", "DHS Report TA18-074A", "Account Monitoring and Controls", "Malicious PowerShell", "Orangeworm Attack Group", "Router & Infrastructure Security", "Brand Monitoring", "Use of Cleartext Protocols", "Disabling Security Tools", "Command and Control", "Suspicious AWS EC2 Activities", "Splunk Enterprise Vulnerability CVE-2018-11409", "Prohibited Traffic Allowed or Protocol Mismatch", "Unusual Processes", "Host Redirection", "Windows Defense Evasion Tactics", "Lateral Movement", "SamSam Ransomware", "Netsh Abuse", "ColdRoot MacOS RAT", "Suspicious AWS Login Activities", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 864000 action.escu.latest_time_offset = 86400 @@ -3620,7 +3577,7 @@ action.escu.full_search_name = ESCU - Get All AWS Activity From IP Address action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities", "Suspicious AWS Traffic", "Command and Control", "Suspicious AWS S3 Activities"] +action.escu.analytic_story = ["AWS Suspicious Provisioning Activities", "Suspicious AWS S3 Activities", "Suspicious AWS Traffic", "Command and Control"] action.escu.fields_required = ["src_ip"] action.escu.earliest_time_offset = 14400 action.escu.latest_time_offset = 0 @@ -3653,28 +3610,55 @@ schedule_window = auto is_visible = false search = sourcetype=WinEventLog:System EventCode=7036 | rex field=Message "The (?[\w\s-]*) service entered the (?\w*) state" | where action="running" | stats earliest(_time) as firstTime, latest(_time) as lastTime by serviceName | outputlookup previously_seen_running_windows_services | stats count -[ESCU - Get Backup Logs For Endpoint] +[ESCU - SMB Traffic Spike - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-24 -action.escu.modification_date = 2017-09-14 +action.escu.creation_date = 2017-08-20 +action.escu.modification_date = 2017-09-10 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = You must be ingesting your backup logs. -action.escu.full_search_name = ESCU - Get Backup Logs For Endpoint -action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 604800 -action.escu.latest_time_offset = 0 -description = This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week. +action.escu.confidence = medium +action.escu.eli5 = Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. +action.escu.how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - SMB Traffic Spike - Rule +action.escu.mappings = {"mitre_attack": ["Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} +action.escu.known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. +action.escu.search_type = detection +action.escu.providing_technologies = ["Bro", "Splunk Stream"] +action.escu.analytic_story = ["Ransomware", "Hidden Cobra Malware", "Emotet Malware (TA18-201A)", "DHS Report TA18-074A"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = SMB Traffic Spike +action.notable = 1 +action.notable.param.nes_fields = src +action.notable.param.rule_description = There was a spike in SMB traffic from $src$. +action.notable.param.rule_title = SMB Traffic Spike from $src$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = src +alert.suppress.period = 28800s +cron_schedule = 0 * * * * +description = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. +dispatch.earliest_time = -7d@d +dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | search sourcetype="netbackup_logs" COMPUTERNAME=$dest$ | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature +search = | tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count [ESCU - Child Processes of Spoolsv.exe - Rule] action.escu = 0 @@ -3739,7 +3723,7 @@ action.escu.full_search_name = ESCU - Get Process responsible for the DNS traffi action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Host Redirection", "Command and Control", "Data Protection", "Brand Monitoring", "Dynamic DNS", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Data Protection", "Suspicious DNS Traffic", "Dynamic DNS", "Brand Monitoring", "Command and Control", "Host Redirection"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 86400 @@ -3766,7 +3750,7 @@ action.escu.mappings = {"mitre_attack": ["Exfiltration", "Exfiltration Over Comm action.escu.known_false_positives = Some users and applications may leverage Dynamic DNS to reach out to some domains on the Internet since dynamic DNS by itself is not malicious, however this activity must be verified. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch", "Data Protection", "Dynamic DNS", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Data Protection", "Suspicious DNS Traffic", "Dynamic DNS", "Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detect hosts connecting to dynamic domain providers action.notable = 1 @@ -3775,7 +3759,7 @@ action.notable.param.rule_description = The search has detected a host making ou action.notable.param.rule_title = Host $src$ detected to make a query to a Dynamic DNS provider action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Information For Port Activity\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -3914,65 +3898,67 @@ schedule_window = auto is_visible = false search = | tstats summariesonly=true allow_old_summaries=true latest(_time) as latestTime from datamodel=Updates where Updates.status=Installed Updates.vendor_product="Microsoft Windows" by Updates.dest Updates.status Updates.vendor_product | rename Updates.dest as Host | rename Updates.status as "Update Status" | rename Updates.vendor_product as Product | eval isOutlier=if(latestTime <= relative_time(now(), "-60d@d"), 1, 0) | `ctime(latestTime)` | search isOutlier=1 | rename latestTime as "Last Update Time", | table Host, "Update Status", Product, "Last Update Time" -[ESCU - Detect Spike in AWS API Activity - Rule] +[ESCU - Get All AWS Activity From City] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-12 -action.escu.modification_date = 2018-04-09 -action.escu.asset_at_risk = AWS Instance +action.escu.creation_date = 2018-03-19 +action.escu.modification_date = 2018-03-19 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.full_search_name = ESCU - Get All AWS Activity From City +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] +action.escu.fields_required = ["City"] +action.escu.earliest_time_offset = 14400 +action.escu.latest_time_offset = 0 +description = This search retrieves all the activity from a specific city and will create a table containing the time, city, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search City=$City$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip, awsRegion, eventName, errorCode + +[ESCU - Email files written outside of the Outlook directory - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-12-13 +action.escu.modification_date = 2018-11-02 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = This search and its corresponding subsearch run through a series of steps, as per the following: \ -\ -1. Retrieves all the AWS CloudTrail log entries that have recorded AWS API calls.\ -\ -1. Kicks off a subsearch that retrieves the same data and pulls out the ARN into a more friendly format.\ -\ -1. Counts the number of API calls per ARN.\ -\ -1. Loads the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ -\ -1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ -\ -1. Renames `apiCalls` as `latestCount`.\ -\ -1. Calculates the new average value for each ARN with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation--weighting the past more heavily than the current.\ -\ -1. Updates the cache file with the latest results.\ -\ -1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ -\ -1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ -\ -1. Filters out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of all the API calls, the number of unique API calls, and the total number of API calls for each of these ARNs. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. -action.escu.full_search_name = ESCU - Detect Spike in AWS API Activity - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} -action.escu.known_false_positives = +action.escu.eli5 = In this search, we are looking for activities consistent with an adversary collecting email data from local machines. The search will detect email files (files with .pst or .ost extensions) created in directories other than the standard Outlook directory (c:\users\username\My Documents\Outlook Files\. +action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Email files written outside of the Outlook directory - Rule +action.escu.mappings = {"mitre_attack": ["Collection", "Email Collection"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"]} +action.escu.known_false_positives = Administrators and users sometimes prefer backing up their email data by moving the email files into a different folder. These attempts will be detected by the search. action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS User Monitoring"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Collection and Staging"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in AWS API Activity +action.correlationsearch.label = Email files written outside of the Outlook directory action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = A spike in the number of AWS API calls by $user$ was detected. -action.notable.param.rule_title = Spike in AWS API activity detected by $user$ -action.notable.param.security_domain = network +action.notable.param.nes_fields = dest, file_path, action, file_name +action.notable.param.rule_description = The system $dest$ has email files outside of the normal Outlook directory +action.notable.param.rule_title = Email files created or modified on $dest$ that are not in the normal Outlook directory +action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Investigate AWS User Activities by user field\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = user -alert.suppress.period = 14400s +alert.suppress.fields = dest, file_path +alert.suppress.period = 86400s cron_schedule = 0 * * * * -description = This search will detect users creating spikes of API activity in your AWS environment. It will also update the cache file that factors in the latest data. +description = The search looks at the change-analysis data model and detects email files created outside the normal Outlook directory. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -3983,34 +3969,33 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventType=AwsApiCall [search sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup api_call_by_user_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | stats values(eventName) as eventNames, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user +search = | tstats `summariesonly` count values(Filesystem.file_path) as file_path min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where (Filesystem.file_name=*.dll OR Filesystem.file_name=*.ost) Filesystem.file_path != "C:\\Users\\*\\My Documents\\Outlook Files\\*" by Filesystem.action Filesystem.process_id Filesystem.file_name Filesystem.dest | `drop_dm_object_name("Filesystem")` | `ctime(firstTime)` | `ctime(lastTime)` -[ESCU - Detect USB device insertion - Rule] +[ESCU - Shim Database Installation With Suspicious Parameters - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-03 -action.escu.modification_date = 2017-11-27 +action.escu.creation_date = 2017-10-03 +action.escu.modification_date = 2017-10-10 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = low -action.escu.eli5 = USB is a common attack vector for delivering or propagating malicious code, or the exfiltration of data. Your corporation may have a policy of not allowing removable media at all, or may only allow approved media to be used on specific hosts by specific users. By logging USB activity from Windows and other endpoints gathered using the Universal Forwarder, you can gain an understanding of what systems might be vulnerable to attack via removable media, or what users might need additional security training. This search is looking for event_id 4656 for failure and 4663 for successful USB read/write attempts from Windows Security Event logs, which is the event code generated when a files are read from and written to a removable storage device -action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. -action.escu.data_models = ["Change_Analysis"] -action.escu.full_search_name = ESCU - Detect USB device insertion - Rule -action.escu.mappings = {"mitre_attack": ["Exfiltration"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.PT", "PR.DS"]} -action.escu.known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. +action.escu.confidence = medium +action.escu.eli5 = This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p. The -q option performs a silent installation with no visible window, status, or warning information. The -p option allows the shim database to contain patches. It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user. +action.escu.how_to_implement = To successfully implement this search, you need to ingest logs with both the process name and command-line from your endpoints. If you are using Sysmon, you will need to have a Splunk Universal Forwarder on each endpoint that you want to collect the data on. You will also need to have to deploy the Sysmon TA on these endpoints and on your search head. You must have at least version 6.0.4 of the Sysmon TA. +action.escu.full_search_name = ESCU - Shim Database Installation With Suspicious Parameters - Rule +action.escu.mappings = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} +action.escu.known_false_positives = None identified action.escu.search_type = detection -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Data Protection"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Windows Persistence Techniques"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect USB device insertion +action.correlationsearch.label = Shim Database Installation With Suspicious Parameters action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = Read/Write attempt to a USB was detected on this host -action.notable.param.rule_title = Read/Write attempt to a USB detected on $dest$ +action.notable.param.nes_fields = dest, user, process +action.notable.param.rule_description = The system $dest$ had a shim database installed. +action.notable.param.rule_title = Shim Database Installation on $dest$ action.notable.param.security_domain = endpoint -action.notable.param.severity = low -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -4019,10 +4004,10 @@ action.risk.param._risk_score = 20 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s +alert.suppress.fields = dest,user +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. +description = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -4033,46 +4018,47 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `ctime(earliest)`| `ctime(latest)` +search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*sdbinst* cmdline="*-p*" cmdline="*-q*" | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - Unsuccessful Netbackup backups - Rule] +[ESCU - Detect malicious requests to exploit JBoss servers - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-06-15 -action.escu.modification_date = 2017-09-12 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2016-10-04 +action.escu.modification_date = 2017-09-23 +action.escu.asset_at_risk = Web Server action.escu.channel = ESCU action.escu.confidence = high -action.escu.eli5 = This search looks across the most recent backup events for each host, and returns those messages that indicate there was a backup failure. -action.escu.how_to_implement = To successfully implement this search you need to obtain data from your backup solution, either from the backup logs on your endpoints or from a central server responsible for performing the backups. If you do not use Netbackup, you can modify this search for your specific backup solution. -action.escu.full_search_name = ESCU - Unsuccessful Netbackup backups - Rule -action.escu.mappings = {"cis20": ["CIS 10"], "nist": ["PR.IP"]} -action.escu.known_false_positives = None identified +action.escu.eli5 = This search looks for HTTP requests for a URL that has been used to exploit JBoss servers. +action.escu.how_to_implement = You must ingest data from the web server or capture network data that contains web specific information with solutions such as Bro or Splunk Stream, and populating the Web data model +action.escu.data_models = ["Web"] +action.escu.full_search_name = ESCU - Detect malicious requests to exploit JBoss servers - Rule +action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Exploitation of Vulnerability"], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 12", "CIS 4", "CIS 18"], "nist": ["ID.RA", "PR.PT", "PR.IP", "DE.AE", "PR.MA", "DE.CM"]} +action.escu.known_false_positives = No known false positives for this detection. action.escu.search_type = detection -action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["Monitor Backup Solution"] +action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +action.escu.analytic_story = ["JBoss Vulnerability", "SamSam Ransomware"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Unsuccessful Netbackup backups +action.correlationsearch.label = Detect malicious requests to exploit JBoss servers action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The system $dest$ attempted a backup but encountered an error. -action.notable.param.rule_title = Failed backup attempt by $dest$ -action.notable.param.security_domain = endpoint +action.notable.param.nes_fields = src, dest_ip +action.notable.param.rule_description = A search for detecting malicious requests made to exploit jmx-console in JBoss servers. The bad requests have a long url length since it serves the payload via the url +action.notable.param.rule_title = Detected malicious requests to exploit JBoss servers +action.notable.param.security_domain = network action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - All backup logs for host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 10 +action.risk.param._risk_score = 80 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s -cron_schedule = 0 7 * * * -description = This search gives you the hosts where a backup was attempted and then failed. -dispatch.earliest_time = -24h@h +alert.suppress.fields = dest,url,src +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search is used to detect malicious HTTP requests crafted to exploit jmx-console in JBoss servers. The malicious requests have a long URL length, as the payload is embedded in the URL. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -4082,56 +4068,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype="netbackup_logs" | stats latest(_time) as latestTime by COMPUTERNAME, MESSAGE | search MESSAGE="An error occurred, failed to backup." | `ctime(latestTime)` | rename COMPUTERNAME as dest, MESSAGE as signature | table latestTime, dest, signature - -[ESCU - Abnormally High AWS Instances Launched by User - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-02-26 -action.escu.modification_date = 2018-02-26 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = In this search, we query CloudTrail logs to look for events where an instance is successfully launched by a particular user. Since we want to detect a high number of instances launched within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances launched by a particular user, as well as the average and standard deviation values. Assign a `threshold_value` in the search. Start with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. For your reference, we then keep only the outliers and calculate the number of standard deviations away the value is from the average. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -action.escu.full_search_name = ESCU - Abnormally High AWS Instances Launched by User - Rule -action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} -action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Abnormally High AWS Instances Launched by User -action.notable = 1 -action.notable.param.nes_fields = userName -action.notable.param.rule_description = An abnormally high number of instances were launched by a user within in a 10-minute window -action.notable.param.rule_title = High Number of instances launched by $userName$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = userName -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = userName -alert.suppress.period = 3600s -cron_schedule = */10 * * * * -description = This search looks for CloudTrail events where a user successfully launches an abnormally high number of instances. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | bucket span=10m _time | stats count AS instances_launched by _time userName | eventstats avg(instances_launched) as total_launched_avg, stdev(instances_launched) as total_launched_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_launched > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m") | eval num_standard_deviations_away = round(abs(instances_launched - total_launched_avg) / total_launched_stdev, 2) | table _time, userName, instances_launched, num_standard_deviations_away, total_launched_avg, total_launched_stdev +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Web where (Web.http_method="GET" OR Web.http_method="HEAD") by Web.http_method, Web.url,Web.url_length Web.src, Web.dest | search Web.url="*jmx-console/HtmlAdaptor?action=invokeOpByName&name=jboss.admin*import*" AND Web.url_length > 200 | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `ctime(lastTime)` | table src, dest_ip, http_method, url, firstTime, lastTime [ESCU - Detect Excessive User Account Lockouts - Rule] action.escu = 0 @@ -4195,7 +4132,7 @@ action.escu.full_search_name = ESCU - Get Vulnerability Logs For Endpoint action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Nessus"] -action.escu.analytic_story = ["Ransomware", "ColdRoot MacOS RAT", "ColdRoot MacOS RAT", "SamSam Ransomware"] +action.escu.analytic_story = ["ColdRoot MacOS RAT", "Ransomware", "SamSam Ransomware", "ColdRoot MacOS RAT"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0 @@ -4305,93 +4242,61 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` sum(All_Traffic.bytes_out) as bytes_out from datamodel=Network_Traffic where All_Traffic.src_category=email_server by All_Traffic.dest_ip _time span=1d | `drop_dm_object_name("All_Traffic")` | eventstats avg(bytes_out) as avg_bytes_out stdev(bytes_out) as stdev_bytes_out | eventstats count as num_data_samples avg(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_avg_bytes_out stdev(eval(if(_time < relative_time(now(), "@d"), bytes_out, null))) as per_source_stdev_bytes_out by dest_ip | eval minimum_data_samples = 4, deviation_threshold = 3 | where num_data_samples >= minimum_data_samples AND bytes_out > (avg_bytes_out + (deviation_threshold * stdev_bytes_out)) AND bytes_out > (per_source_avg_bytes_out + (deviation_threshold * per_source_stdev_bytes_out)) AND _time >= relative_time(now(), "@d") | eval num_standard_deviations_away_from_server_average = round(abs(bytes_out - avg_bytes_out) / stdev_bytes_out, 2), num_standard_deviations_away_from_client_average = round(abs(bytes_out - per_source_avg_bytes_out) / per_source_stdev_bytes_out, 2) | table dest_ip, _time, bytes_out, avg_bytes_out, per_source_avg_bytes_out, num_standard_deviations_away_from_server_average, num_standard_deviations_away_from_client_average -[ESCU - Identify New User Accounts - Rule] +[ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-05 -action.escu.modification_date = 2017-09-12 -action.escu.asset_at_risk = Domain Server +action.escu.creation_date = 2018-04-26 +action.escu.modification_date = 2018-05-07 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = Adversaries will often seek to create new user accounts as a means of maintaining access to a target environment. Using this search, we identify accounts created in the last week by comparing the start date in the Identity_Management data model against the current time. -action.escu.how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. -action.escu.data_models = ["Identity_Management"] -action.escu.full_search_name = ESCU - Identify New User Accounts - Rule -action.escu.mappings = {"mitre_attack": ["Valid Accounts"], "cis20": ["CIS 16"], "nist": ["PR.IP"]} -action.escu.known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. -action.escu.search_type = detection -action.escu.providing_technologies = ["Active Directory"] -action.escu.analytic_story = ["Account Monitoring and Controls"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Identify New User Accounts -action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = Using the identities lookup and macro from Enterprise Security to identify (report) new users (6 month period) and temp users (3 months until account expiration) -action.notable.param.rule_title = Identify Temporary Users -action.notable.param.security_domain = access -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Logon Rights Modifications For Endpoint\n - ESCU - Get Logon Rights Modifications For User\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = identity -alert.suppress.period = 86400s -cron_schedule = 0 0 * * * -description = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. -dispatch.earliest_time = -24h@h -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `ctime(endDate)` | `ctime(startDate)`| table identity empStatus endDate startDate - -[ESCU - Detect S3 access from a new IP - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-06-25 -action.escu.modification_date = 2018-06-28 -action.escu.asset_at_risk = S3 Bucket -action.escu.channel = ESCU -action.escu.confidence = low -action.escu.eli5 = Here the subsearch executes first and returns all successful S3 bucket-access attempts (HTTP code "200") within the last hour. It groups the results by the earliest and latest times it has seen a remote IP accessing a particular bucket. It appends this information to the historical data from the lookup file and then recalculates the `firstTime` and `lastTime` field for each remote IP accessing an S3 bucket. Next, it returns only those remote IP addresses that have first been seen accessing a specific bucket within the past hour. This is combined with the main search to return the time, bucket name, source IP, city, and country operations performed, as well as the requested URI of the resource -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your S3 access logs' inputs. This search works best when you run the "Previously Seen S3 Bucket Access by Remote IP" support search once to create a history of previously seen remote IPs and bucket names. -action.escu.full_search_name = ESCU - Detect S3 access from a new IP - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13", "CIS 14"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -action.escu.known_false_positives = S3 buckets can be accessed from any IP, as long as it can make a successful connection. This will be a false postive, since the search is looking for a new IP within the past hour +action.escu.eli5 = This search retrieves all the VPC Flow log entries that have recorded a blocked outbound network connection originating from your AWS environment. Then it kicks off a subsearch, which looks at the same data and performs the following series of steps: \ +\ +1. Counts the number of blocked outbound connections by each source IP\ +\ +1. Loads the cache file that contains the number of data points, the count from the latest hour, the average blocked connections, and the standard deviation for each source IP.\ +\ +1. Drops the count from the latest hour, since it is not necessary, and merges the rest of the data with the results of the stats command. \ +\ +1. Renames `numberOfBlockedConnections` as `latestCount`.\ +\ +1. Calculates the new average value for each source IP with the latest count, weighting the past much more heavily than the current hour. It does the same for the standard deviation, weighting the past more heavily than the current.\ +\ +1. Updates the cache file with the latest results.\ +\ +1. Sets the minimum threshold for the number of data points and sets the number of standard deviations away from the mean it must be to be considered a spike.\ +\ +1. Makes a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and the count is a sufficient number of standard deviations away from the average.\ +\ +1. Filters out anything that it determines is not a spike and returns the list of source IPs to the main search. The main search subsequently gets the list of all destination IPs for which the traffic was blocked, the network interface ID, the number of unique destination IP, and the total number of blocked connections for each of these source IP addresses. Finally, it looks up the average and standard deviation and returns both the average and the number of standard deviations the spike is from the average. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your VPC Flow logs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the number of data points required to meet the definition of "spike." The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of Blocked Outbound Connection" support search once to create a history of previously seen blocked outbound connections. +action.escu.full_search_name = ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule +action.escu.mappings = {"mitre_attack": ["Exfiltration", "Command and Control"], "kill_chain_phases": ["Actions on Objectives", "Command and Control"], "cis20": ["CIS 11"], "nist": ["DE.AE", "DE.CM", "PR.AC"]} +action.escu.known_false_positives = The false-positive rate may vary based on the values of`dataPointThreshold` and `deviationThreshold`. Additionally, false positives may result when AWS administrators roll out policies enforcing network blocks, causing sudden increases in the number of blocked outbound connections. action.escu.search_type = detection action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities"] +action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command and Control"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect S3 access from a new IP +action.correlationsearch.label = Detect Spike in blocked Outbound Traffic from your AWS action.notable = 1 -action.notable.param.nes_fields = bucket_name, src_ip -action.notable.param.rule_description = A remote IP, $src_ip$, has made a successful connection with an S3 $bucket_name$. -action.notable.param.rule_title = S3 bucket $bucketName$ was accessed by a new $src_ip$ +action.notable.param.nes_fields = src_ip +action.notable.param.rule_description = A spike in the blocked outbound connection is detected from source $src_ip$. +action.notable.param.rule_title = Spike in blocked outbound network connections from $src_ip$ detected. action.notable.param.security_domain = network -action.notable.param.severity = low -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src_ip action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = bucket_name, src_ip -alert.suppress.period = 86400s -cron_schedule = 5 * * * * -description = This search looks at S3 bucket-access logs and detects new or previously unseen remote IP addresses that have successfully accessed an S3 bucket. +alert.suppress.fields = src_ip +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search will detect spike in blocked outbound network connections originating from within your AWS environment. It will also update the cache file that factors in the latest data. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -4402,33 +4307,34 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:s3:accesslogs http_status=200 [search sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) as firstTime latest(_time) as lastTime by bucket_name remote_ip | inputlookup append=t previously_seen_S3_access_from_remote_ip.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip.csv | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | convert ctime(firstTime) ctime(lastTime) | table bucket_name remote_ip]| iplocation remote_ip |rename remote_ip as src_ip | table _time bucket_name src_ip City Country operation request_uri +search = sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) [search sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | stats count as numberOfBlockedConnections by src_ip | inputlookup baseline_blocked_outbound_connections append=t | fields - latestCount | stats values(*) as * by src_ip | rename numberOfBlockedConnections as latestCount | eval newAvgBlockedConnections=avgBlockedConnections + (latestCount-avgBlockedConnections)/720 | eval newStdevBlockedConnections=sqrt(((pow(stdevBlockedConnections, 2)*719 + (latestCount-newAvgBlockedConnections)*(latestCount-avgBlockedConnections))/720)) | eval avgBlockedConnections=coalesce(newAvgBlockedConnections, avgBlockedConnections), stdevBlockedConnections=coalesce(newStdevBlockedConnections, stdevBlockedConnections), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | eval dataPointThreshold = 5, deviationThreshold = 3 | eval isSpike=if((latestCount > avgBlockedConnections+deviationThreshold*stdevBlockedConnections) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | table src_ip] | stats values(dest_ip) as "Blocked Destination IPs", values(interface_id) as "resourceId" count as numberOfBlockedConnections, dc(dest_ip) as uniqueDestConnections by src_ip -[ESCU - Shim Database Installation With Suspicious Parameters - Rule] +[ESCU - Detect USB device insertion - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-10-03 -action.escu.modification_date = 2017-10-10 +action.escu.creation_date = 2017-08-03 +action.escu.modification_date = 2017-11-27 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search looks for the execution of sdbinst.exe with command-line arguments of -q and -p. The -q option performs a silent installation with no visible window, status, or warning information. The -p option allows the shim database to contain patches. It will return the count, the first time, and the last time these command-line arguments were seen on each endpoint and by each user. -action.escu.how_to_implement = To successfully implement this search, you need to ingest logs with both the process name and command-line from your endpoints. If you are using Sysmon, you will need to have a Splunk Universal Forwarder on each endpoint that you want to collect the data on. You will also need to have to deploy the Sysmon TA on these endpoints and on your search head. You must have at least version 6.0.4 of the Sysmon TA. -action.escu.full_search_name = ESCU - Shim Database Installation With Suspicious Parameters - Rule -action.escu.mappings = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -action.escu.known_false_positives = None identified +action.escu.confidence = low +action.escu.eli5 = USB is a common attack vector for delivering or propagating malicious code, or the exfiltration of data. Your corporation may have a policy of not allowing removable media at all, or may only allow approved media to be used on specific hosts by specific users. By logging USB activity from Windows and other endpoints gathered using the Universal Forwarder, you can gain an understanding of what systems might be vulnerable to attack via removable media, or what users might need additional security training. This search is looking for event_id 4656 for failure and 4663 for successful USB read/write attempts from Windows Security Event logs, which is the event code generated when a files are read from and written to a removable storage device +action.escu.how_to_implement = To successfully implement this search, you must ingest Windows Security Event logs and track event code 4663 and 4656. Ensure that the field from the event logs is being mapped to the result_id field in the Change_Analysis data model. To minimize the alert volume, this search leverages the Assets and Identity framework to filter out events from those assets not marked high priority in the Enterprise Security Assets and Identity Framework. +action.escu.data_models = ["Change_Analysis"] +action.escu.full_search_name = ESCU - Detect USB device insertion - Rule +action.escu.mappings = {"mitre_attack": ["Exfiltration"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.PT", "PR.DS"]} +action.escu.known_false_positives = Legitimate USB activity will also be detected. Please verify and investigate as appropriate. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Persistence Techniques"] +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Data Protection"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Shim Database Installation With Suspicious Parameters +action.correlationsearch.label = Detect USB device insertion action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = The system $dest$ had a shim database installed. -action.notable.param.rule_title = Shim Database Installation on $dest$ +action.notable.param.nes_fields = dest +action.notable.param.rule_description = Read/Write attempt to a USB was detected on this host +action.notable.param.rule_title = Read/Write attempt to a USB detected on $dest$ action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.severity = low +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -4437,10 +4343,10 @@ action.risk.param._risk_score = 20 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,user -alert.suppress.period = 14400s +alert.suppress.fields = dest +alert.suppress.period = 86400s cron_schedule = 0 * * * * -description = This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere. +description = The search is used to detect hosts that generate Windows Event ID 4663 for successful attempts to write to or read from a removable storage and Event ID 4656 for failures, which occurs when a USB drive is plugged in. In this scenario we are querying the Change_Analysis data model to look for Windows Event ID 4656 or 4663 where the priority of the affected host is marked as high in the ES Assets and Identity Framework. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -4451,7 +4357,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*sdbinst* cmdline="*-p*" cmdline="*-q*" | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` +search = | tstats `summariesonly` count earliest(_time) AS earliest latest(_time) AS latest from datamodel=Change_Analysis where (nodename = All_Changes) All_Changes.result="Removable Storage device" (All_Changes.result_id=4663 OR All_Changes.result_id=4656) (All_Changes.src_priority=high) by All_Changes.dest | `drop_dm_object_name("All_Changes")`| `ctime(earliest)`| `ctime(latest)` [ESCU - Detect Large Outbound ICMP Packets - Rule] action.escu = 0 @@ -4526,44 +4432,44 @@ schedule_window = auto is_visible = false search = | search sourcetype=WinEventLog:Security (EventCode=4718 OR EventCode=4717) user=$user$ | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature -[ESCU - Abnormally High AWS Instances Terminated by User - Rule] +[ESCU - Attempt To Stop Security Service - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-02-26 -action.escu.modification_date = 2018-02-26 -action.escu.asset_at_risk = AWS Instance +action.escu.creation_date = 2018-04-09 +action.escu.modification_date = 2017-09-15 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a `threshold_value` in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier to 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. We then filter out outliers with a value of 1 and show only those instance-termination events that happened within the previous 10 minutes. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -action.escu.full_search_name = ESCU - Abnormally High AWS Instances Terminated by User - Rule -action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} -action.escu.known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. +action.escu.confidence = high +action.escu.eli5 = This search looks for the processes **net.exe** and **sc.exe** with a parameter of `"stop"`. It then searches a list of security-related services included in a lookup file for matches on the command line. Results are subsequently returned in table format. The included lookup file can be modified to update the services to monitor. +action.escu.how_to_implement = You must be ingesting logs with both the process name and command line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., +action.escu.full_search_name = ESCU - Attempt To Stop Security Service - Rule +action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS EC2 Activities"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Disabling Security Tools"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Abnormally High AWS Instances Terminated by User +action.correlationsearch.label = Attempt To Stop Security Service action.notable = 1 -action.notable.param.nes_fields = userName -action.notable.param.rule_description = An abnormally high number of instances were terminated by a user in a 10-minute window -action.notable.param.rule_title = High number of instances terminated by $userName$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Instance Details by instanceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Investigate AWS activities via region name\n"} +action.notable.param.nes_fields = dest, process, user +action.notable.param.rule_description = Attempt to stop a security-related service on $dest$ +action.notable.param.rule_title = Attempt to Stop Security Service On $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = userName -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 30 +action.risk.param._risk_object = src +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = userName -alert.suppress.period = 3600s -cron_schedule = */10 * * * * -description = This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window -dispatch.earliest_time = -30d@d +alert.suppress.fields = dest, user +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for attempts to stop security-related services on the endpoint. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -4573,7 +4479,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev +search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational (process=net.exe OR process=sc.exe) cmdline="* stop *" | lookup security_services_lookup service as cmdline OUTPUTNEW category, description | search category=security | table _time, dest, user, parent_process, cmdline, description [ESCU - Scheduled Task Name Used by Dragonfly Threat Actors - Rule] action.escu = 0 @@ -4638,7 +4544,7 @@ action.escu.full_search_name = ESCU - Investigate Web Activity From src_ip action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.analytic_story = ["Splunk Enterprise Vulnerability CVE-2018-11409", "ColdRoot MacOS RAT", "ColdRoot MacOS RAT", "Dynamic DNS"] +action.escu.analytic_story = ["ColdRoot MacOS RAT", "Dynamic DNS", "Splunk Enterprise Vulnerability CVE-2018-11409", "ColdRoot MacOS RAT"] action.escu.fields_required = ["src_ip"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 3600 @@ -4662,7 +4568,7 @@ action.escu.full_search_name = ESCU - Get Risk Modifiers For User action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Apache Struts Vulnerability", "Credential Dumping", "Suspicious Windows Registry Activities", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "Host Redirection", "Collection and Staging", "Ransomware", "Windows Service Abuse", "Command and Control", "ColdRoot MacOS RAT", "Orangeworm Attack Group", "SQL Injection", "Emotet Malware (TA18-201A)", "Disabling Security Tools", "Use of Cleartext Protocols", "Suspicious WMI Use", "Prohibited Traffic Allowed or Protocol Mismatch", "Hidden Cobra Malware", "Unusual Processes", "Data Protection", "Brand Monitoring", "Asset Tracking", "Account Monitoring and Controls", "Windows Privilege Escalation", "ColdRoot MacOS RAT", "SamSam Ransomware", "Monitor for Unauthorized Software", "Windows Persistence Techniques", "Malicious PowerShell", "DNS Amplification Attacks", "Spectre And Meltdown Vulnerabilities", "Dynamic DNS", "Lateral Movement", "Suspicious DNS Traffic", "Splunk Enterprise Vulnerability", "Netsh Abuse", "Windows Log Manipulation", "Monitor Backup Solution", "Router & Infrastructure Security", "Suspicious Emails", "Suspicious MSHTA Activity", "JBoss Vulnerability", "Monitor for Updates", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Suspicious WMI Use", "ColdRoot MacOS RAT", "JBoss Vulnerability", "Data Protection", "Ransomware", "Suspicious MSHTA Activity", "Hidden Cobra Malware", "Windows Log Manipulation", "Suspicious Windows Registry Activities", "Splunk Enterprise Vulnerability", "Windows File Extension and Association Abuse", "Suspicious DNS Traffic", "Windows Persistence Techniques", "Dynamic DNS", "Apache Struts Vulnerability", "Credential Dumping", "Monitor for Updates", "Spectre And Meltdown Vulnerabilities", "Collection and Staging", "Emotet Malware (TA18-201A)", "Monitor for Unauthorized Software", "DNS Amplification Attacks", "Suspicious Emails", "SQL Injection", "Monitor Backup Solution", "Windows Service Abuse", "Windows Privilege Escalation", "Asset Tracking", "DHS Report TA18-074A", "Account Monitoring and Controls", "Malicious PowerShell", "Orangeworm Attack Group", "Router & Infrastructure Security", "Brand Monitoring", "Use of Cleartext Protocols", "Disabling Security Tools", "Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch", "Unusual Processes", "Host Redirection", "Lateral Movement", "SamSam Ransomware", "Netsh Abuse", "ColdRoot MacOS RAT", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] action.escu.fields_required = ["user"] action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0 @@ -4686,7 +4592,7 @@ action.escu.full_search_name = ESCU - Get Process Info action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Credential Dumping", "Suspicious Windows Registry Activities", "DHS Report TA18-074A", "Suspicious Command-Line Executions", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Collection and Staging", "Ransomware", "Windows Service Abuse", "Command and Control", "Orangeworm Attack Group", "Emotet Malware (TA18-201A)", "Disabling Security Tools", "Suspicious WMI Use", "Hidden Cobra Malware", "Unusual Processes", "Windows Privilege Escalation", "SamSam Ransomware", "Monitor for Unauthorized Software", "Windows Persistence Techniques", "Malicious PowerShell", "Lateral Movement", "Netsh Abuse", "Windows Log Manipulation", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Suspicious WMI Use", "Ransomware", "Suspicious MSHTA Activity", "Hidden Cobra Malware", "Windows Log Manipulation", "Suspicious Windows Registry Activities", "Windows File Extension and Association Abuse", "Windows Persistence Techniques", "Credential Dumping", "Collection and Staging", "Emotet Malware (TA18-201A)", "Monitor for Unauthorized Software", "Windows Service Abuse", "Suspicious Command-Line Executions", "Windows Privilege Escalation", "DHS Report TA18-074A", "Malicious PowerShell", "Orangeworm Attack Group", "Disabling Security Tools", "Command and Control", "Unusual Processes", "Windows Defense Evasion Tactics", "Lateral Movement", "SamSam Ransomware", "Netsh Abuse", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] action.escu.fields_required = ["process", "dest"] action.escu.earliest_time_offset = 7200 action.escu.latest_time_offset = 7200 @@ -4719,43 +4625,43 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles | stats count -[ESCU - Detect new API calls from user roles - Rule] +[ESCU - Detect New Open S3 buckets - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-04-01 -action.escu.modification_date = 2018-04-16 -action.escu.asset_at_risk = AWS Instance +action.escu.creation_date = 2018-07-25 +action.escu.modification_date = 2018-07-25 +action.escu.asset_at_risk = S3 Bucket action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is `AssumedRole`. It then appends the historical data to those results in the lookup file. Next, it recalculates the `earliest` and `latest` fields for each user role, as well as the name of the API call, and returns only those roles and API calls that have first been seen in the past hour. This is combined with the main search to return the values of API calls, name of the user role, and the earliest and latest time of this activity. It is worth noting that the name of the role of a particular user is parsed as "userName" in the CloudTrail logs. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. -action.escu.full_search_name = ESCU - Detect new API calls from user roles - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. +action.escu.eli5 = This search queries CloudTrail logs for events with S3 bucket access controls given to the "All Users" group, which allows anyone in the world access to the resource. This search generates a table displaying the time when the bucket was made public, the permission of the S3 bucket, the bucket name, and the ARN of the user who created the bucket. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail inputs. The threshold value should be tuned to your environment. +action.escu.full_search_name = ESCU - Detect New Open S3 buckets - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} +action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. action.escu.search_type = detection action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS User Monitoring"] +action.escu.analytic_story = ["Suspicious AWS S3 Activities"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect new API calls from user roles +action.correlationsearch.label = Detect New Open S3 buckets action.notable = 1 action.notable.param.nes_fields = user -action.notable.param.rule_description = A new API call made by $user$ has been detected. This API activity has either never been seen before or has not been seen within the last hour. -action.notable.param.rule_title = New API call by $user$ detected -action.notable.param.security_domain = endpoint +action.notable.param.rule_description = An open/public S3 bucket, $bucketName$, was created by $user$. +action.notable.param.rule_title = Public S3 bucket $bucketName$ created by $user$ +action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Investigate AWS User Activities by user field\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = user action.risk.param._risk_object_type = user -action.risk.param._risk_score = 10 +action.risk.param._risk_score = 70 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = user +alert.suppress.fields = user,bucketName alert.suppress.period = 86400s -cron_schedule = 30 * * * * -description = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. +cron_schedule = 5 * * * * +description = This search looks for CloudTrail events where a user has created an open/public S3 bucket. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -4766,7 +4672,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `ctime(earliest)` | `ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `ctime(earliest)` | `ctime(latest)` +search = sourcetype=aws:cloudtrail AllUsers eventName=PutBucketAcl | spath output=userIdentityArn path=userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | spath output=aclControlList path=requestParameters.AccessControlPolicy.AccessControlList | spath input=aclControlList output=grantee path=Grant{} | mvexpand grantee | spath input=grantee | search Grantee.URI=*AllUsers | rename userIdentityArn as user| table _time, src,awsRegion Permission, Grantee.URI, bucketName, user [ESCU - Hiding Files And Directories With Attrib.exe - Rule] action.escu = 0 @@ -4783,7 +4689,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Persistence"], "kil action.escu.known_false_positives = Some applications and users may legitimately use attrib.exe to interact with the files. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Windows Persistence Techniques"] +action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Defense Evasion Tactics"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Hiding Files And Directories With Attrib.exe action.notable = 1 @@ -4792,7 +4698,7 @@ action.notable.param.rule_description = Attrib.exe is often used by attackers to action.notable.param.rule_title = Suspicious usage of attrib.exe on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -4867,28 +4773,56 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count from datamodel=Web where Web.dest_category=web_server AND (Web.url_length > 1024 OR Web.http_user_agent_length > 200) by Web.src Web.dest Web.url Web.url_length Web.http_user_agent | `drop_dm_object_name("Web")` | eval num_sql_cmds=mvcount(split(url, "alter%20table")) + mvcount(split(url, "between")) + mvcount(split(url, "create%20table")) + mvcount(split(url, "create%20database")) + mvcount(split(url, "create%20index")) + mvcount(split(url, "create%20view")) + mvcount(split(url, "delete")) + mvcount(split(url, "drop%20database")) + mvcount(split(url, "drop%20index")) + mvcount(split(url, "drop%20table")) + mvcount(split(url, "exists")) + mvcount(split(url, "exec")) + mvcount(split(url, "group%20by")) + mvcount(split(url, "having")) + mvcount(split(url, "insert%20into")) + mvcount(split(url, "inner%20join")) + mvcount(split(url, "left%20join")) + mvcount(split(url, "right%20join")) + mvcount(split(url, "full%20join")) + mvcount(split(url, "select")) + mvcount(split(url, "distinct")) + mvcount(split(url, "select%20top")) + mvcount(split(url, "union")) + mvcount(split(url, "xp_cmdshell")) - 24 | where num_sql_cmds > 3 -[ESCU - Get Web Session Information via session_id] +[ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-10-08 -action.escu.modification_date = 2018-10-08 +action.escu.creation_date = 2018-03-16 +action.escu.modification_date = 2018-03-16 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server. -action.escu.full_search_name = ESCU - Get Web Session Information via session_id -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Splunk Stream"] -action.escu.analytic_story = ["Web Fraud Detection"] -action.escu.fields_required = ["session_id"] -action.escu.earliest_time_offset = 3600 -action.escu.latest_time_offset = 3600 -description = This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code. +action.escu.confidence = medium +action.escu.eli5 = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. +action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ +\ + This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen IP Address +action.notable = 1 +action.notable.param.nes_fields = src_ip +action.notable.param.rule_description = Your AWS infrastructure was provisioned from an IP, $src_ip$, which has never before been seen provisioning your infrastructure. +action.notable.param.rule_title = AWS Provision Activity From $src_ip$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From City\n - ESCU - Get All AWS Activity From Country\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get All AWS Activity From Region\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src_ip +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = src_ip +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | search sourcetype=stream:http $session_id$ | stats values(url) values(http_user_agent) by src_ip status +search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode [ESCU - Execution of File With Spaces Before Extension - Rule] action.escu = 0 @@ -4953,7 +4887,7 @@ action.escu.full_search_name = ESCU - Investigate Network Traffic From src_ip action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Splunk Stream", "Bro", "Palo Alto Firewall"] -action.escu.analytic_story = ["Splunk Enterprise Vulnerability CVE-2018-11409", "ColdRoot MacOS RAT", "ColdRoot MacOS RAT"] +action.escu.analytic_story = ["ColdRoot MacOS RAT", "Splunk Enterprise Vulnerability CVE-2018-11409", "ColdRoot MacOS RAT"] action.escu.fields_required = ["src_ip"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 3600 @@ -5014,29 +4948,6 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*") by Registry.dest , Registry.status, Registry.user | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(Registry)` -[ESCU - Get All AWS Activity From Region] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-03-19 -action.escu.modification_date = 2018-03-19 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -action.escu.full_search_name = ESCU - Get All AWS Activity From Region -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -action.escu.fields_required = ["Region"] -action.escu.earliest_time_offset = 14400 -action.escu.latest_time_offset = 0 -description = This search retrieves all the activity from a specific geographic region and will create a table containing the time, geographic region, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search Region=$Region$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, Region, user, userName, userType, src_ip, awsRegion, eventName, errorCode - [ESCU - EC2 Instance Modified With Previously Unseen User - Rule] action.escu = 0 action.escu.enabled = 1 @@ -5159,56 +5070,6 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change_Analysis where All_Changes.result_id=4720 OR All_Changes.result_id=4726 by All_Changes.result_id All_Changes.user All_Changes.dest | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name("All_Changes")` | transaction user maxspan=240m | search result_id=4720 result_id=4726 -[ESCU - Remote WMI Command Attempt - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-01-13 -action.escu.modification_date = 2018-12-03 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Remote WMI Command Attempt - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -action.escu.known_false_positives = Administrators may use this legitimately to gather info from remote systems. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious WMI Use"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote WMI Command Attempt -action.notable = 1 -action.notable.param.nes_fields = dest,user,process_name -action.notable.param.rule_description = This search looks for wmic.exe being launched with parameters to operate on remote systems. -action.notable.param.rule_title = Endpoint - Remote WMI command attempt -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Get Sysmon WMI Activity for Host\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,user,process_name -alert.suppress.period = 28800s -cron_schedule = 50 * * * * -description = This search looks for wmic.exe being launched with parameters to operate on remote systems. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` - [ESCU - AWS Cloud Provisioning From Previously Unseen Region - Rule] action.escu = 0 action.escu.enabled = 1 @@ -5384,44 +5245,43 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Authentication where Authentication.signature_id=4624 Authentication.app=win:remote by Authentication.src Authentication.dest Authentication.app Authentication.user Authentication.signature Authentication.src_nt_domain | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name("Authentication")`| table firstTime lastTime src src_nt_domain dest user app count | sort count -[ESCU - Common Ransomware Extensions - Rule] +[ESCU - Detect new user AWS Console Login - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-21 -action.escu.modification_date = 2018-11-15 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-02-26 +action.escu.modification_date = 2018-02-26 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated by ransomware. -action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Common Ransomware Extensions - Rule -action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. +action.escu.confidence = medium +action.escu.eli5 = In this search, we query CloudTrail logs to look for events that indicate that a user has attempted to log in to the AWS console and group the events using ARN value. Using the `previously_seen_users_console_logins.csv` lookup file created using the support search, we compare the ARN to all the previously seen users logging into the AWS console. The `eval` and `if` functions determine whether the earliest time we see this user ARN was seen within the last hour. The alert will be fired only when a user is seen for first time in the last hour. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the "Previously seen users in CloudTrail" support search only once to create a baseline of previously seen IAM users within the last 30 days +action.escu.full_search_name = ESCU - Detect new user AWS Console Login - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["DE.DP", "DE.AE"]} +action.escu.known_false_positives = When a legitimate new user logins for the first time, this activity will be detected. Check how old the account is and verify that the user activity is legitimate. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS Login Activities"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Common Ransomware Extensions +action.correlationsearch.label = Detect new user AWS Console Login action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file modification was detected on $dest$ with an extension commonly used by ransomware. -action.notable.param.rule_title = Ransomware Extension detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.nes_fields = arn +action.notable.param.rule_description = A new user has logged into the AWS console +action.notable.param.rule_title = AWS Console Login by New User +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 +action.risk.param._risk_object = arn +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = The search looks for file modifications with extensions commonly used by Ransomware +alert.suppress.fields = arn +alert.suppress.period = 86400s +cron_schedule = 5 * * * * +description = This search looks for CloudTrail events wherein a console login event by a user was recorded within the last hour, then compares the event to a lookup file of previously seen users (by ARN values) who have logged into the console. The alert is fired if the user has logged into the console for the first time within the last hour dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -5432,7 +5292,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` +search = sourcetype=aws:cloudtrail eventName=ConsoleLogin | rename userIdentity.arn as arn |stats earliest(_time) as earliest latest(_time) as latest by arn | inputlookup append=t previously_seen_users_console_logins.csv | stats min(earliest) as earliest max(latest) as latest by arn | outputlookup previously_seen_users_console_logins.csv | eval userStatus=if(earliest >= relative_time(now(), "-70m@m"), "First Time Logging into AWS Console","Previously Seen User") | convert ctime(earliest) ctime(latest) | where userStatus ="First Time Logging into AWS Console" [ESCU - EC2 Instance Started With Previously Unseen User - Rule] action.escu = 0 @@ -5483,29 +5343,6 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | stats earliest(_time) as firstTime latest(_time) as lastTime by userIdentity.arn | rename userIdentity.arn as arn | inputlookup append=t previously_seen_ec2_launches_by_user.csv | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_launches_by_user.csv | eval newUser=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newUser=1 | `ctime(firstTime)` | `ctime(lastTime)` | rename arn as userIdentity.arn | table userIdentity.arn] | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest, userIdentity.arn as user | table _time, user, dest, instanceType -[ESCU - Identify Systems Using Remote Desktop] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-04-18 -action.escu.modification_date = 2017-09-15 -action.escu.channel = ESCU -action.escu.eli5 = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. It does this by looking for the process in the Application_State data model. -action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. -action.escu.data_models = ["Application_State"] -action.escu.full_search_name = ESCU - Identify Systems Using Remote Desktop -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Lateral Movement"] -description = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count from datamodel=Application_State where All_Application_State.process="*mstsc.exe*" by All_Application_State.dest All_Application_State.process | `drop_dm_object_name("All_Application_State")` | sort - count - [ESCU - Previously seen command line arguments] action.escu = 0 action.escu.enabled = 1 @@ -5518,7 +5355,7 @@ action.escu.full_search_name = ESCU - Previously seen command line arguments action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Hidden Cobra Malware", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Hidden Cobra Malware", "Suspicious Command-Line Executions", "DHS Report TA18-074A", "Orangeworm Attack Group", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] description = This search looks for command-line arguments where `cmd.exe /c` is used to execute a program, then creates a baseline of the earliest and latest times we have encountered this command-line argument in our dataset within the last 30 days. dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m @@ -5528,44 +5365,45 @@ schedule_window = auto is_visible = false search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational process=cmd.exe cmdline="* /c *" | stats earliest(_time) as firstTime latest(_time) as lastTime by cmdline | outputlookup previously_seen_cmd_line_arguments | stats count -[ESCU - AWS Network Access Control List Deleted - Rule] +[ESCU - Remote WMI Command Attempt - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-01-08 -action.escu.modification_date = 2017-01-10 -action.escu.asset_at_risk = AWS Instance +action.escu.creation_date = 2017-01-13 +action.escu.modification_date = 2018-12-03 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = The search looks for CloudTrail events to detect whether any network ACLs have been deleted and gives you values of error messages and error codes (if any), user details, user source IP, the user who initiated this request, and the name of the event. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -action.escu.full_search_name = ESCU - AWS Network Access Control List Deleted - Rule -action.escu.mappings = {"mitre_attack": ["Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11"], "nist": ["DE.DP", "DE.AE"]} -action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL. +action.escu.eli5 = Many a times, attackers leverage native Windows utilities that are designed to help administrators better manage their systems, infrastructure, and auditing, but are instead leveraged for malicious purposes. In this case, we are looking for instances of wmic.exe being run with various parameters that are not typically used by administrators. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Remote WMI Command Attempt - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instrumentation"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +action.escu.known_false_positives = Administrators may use this legitimately to gather info from remote systems. action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Network ACL Activity"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Suspicious WMI Use"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Network Access Control List Deleted +action.correlationsearch.label = Remote WMI Command Attempt action.notable = 1 -action.notable.param.nes_fields = src, src_user, eventName -action.notable.param.rule_description = AWS network ACL has been deleted by $src_user. -action.notable.param.rule_title = AWS Network ACL deleted by $src_user$ -action.notable.param.security_domain = network +action.notable.param.nes_fields = dest,user,process_name +action.notable.param.rule_description = This search looks for wmic.exe being launched with parameters to operate on remote systems. +action.notable.param.rule_title = Endpoint - Remote WMI command attempt +action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network ACL Details from ID\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Get Sysmon WMI Activity for Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 80 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = src_user -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. -dispatch.earliest_time = -1d@d +alert.suppress.fields = dest,user,process_name +alert.suppress.period = 28800s +cron_schedule = 50 * * * * +description = This search looks for wmic.exe being launched with parameters to operate on remote systems. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -5575,7 +5413,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `ctime(lastTime)` | `ctime(firstTime)` +search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=wmic.exe AND Processes.process= */node* by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` [ESCU - Process Execution via WMI - Rule] action.escu = 0 @@ -5626,45 +5464,67 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) parent_process=*WmiPrvSE.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, parent_process, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] +[ESCU - Get User Information from Identity Table] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-07-08 -action.escu.modification_date = 2017-09-18 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2017-04-10 +action.escu.modification_date = 2017-09-20 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search you must have populated the identity table with information about your users. +action.escu.full_search_name = ESCU - Get User Information from Identity Table +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["Splunk Enterprise Security"] +action.escu.analytic_story = ["Suspicious WMI Use", "ColdRoot MacOS RAT", "JBoss Vulnerability", "Data Protection", "Ransomware", "Suspicious MSHTA Activity", "AWS Network ACL Activity", "Hidden Cobra Malware", "Windows Log Manipulation", "Suspicious Windows Registry Activities", "Splunk Enterprise Vulnerability", "Windows File Extension and Association Abuse", "Suspicious DNS Traffic", "Windows Persistence Techniques", "Dynamic DNS", "Suspicious AWS S3 Activities", "Apache Struts Vulnerability", "Credential Dumping", "Monitor for Updates", "Spectre And Meltdown Vulnerabilities", "Collection and Staging", "Emotet Malware (TA18-201A)", "Monitor for Unauthorized Software", "Suspicious Emails", "SQL Injection", "Windows Service Abuse", "Suspicious Command-Line Executions", "Windows Privilege Escalation", "Asset Tracking", "DHS Report TA18-074A", "Account Monitoring and Controls", "Malicious PowerShell", "Orangeworm Attack Group", "Router & Infrastructure Security", "Brand Monitoring", "Use of Cleartext Protocols", "Disabling Security Tools", "Command and Control", "Suspicious AWS EC2 Activities", "Unusual Processes", "Host Redirection", "Windows Defense Evasion Tactics", "Lateral Movement", "SamSam Ransomware", "Netsh Abuse", "ColdRoot MacOS RAT", "Suspicious AWS Login Activities", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.fields_required = ["user"] +action.escu.earliest_time_offset = 864000 +action.escu.latest_time_offset = 86400 +description = Gather more information about the user identified in the Notable Event. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | `identities` | search identity=$user$ | table _time, identity, first, last, email, category, watchlist + +[ESCU - AWS Network Access Control List Deleted - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-01-08 +action.escu.modification_date = 2017-01-10 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = Clients should be resolving their DNS requests via a trusted DNS server. This search will identify DNS queries being sent to unauthorized DNS servers by comparing the destination and source of the traffic with assets marked as DNS servers. -action.escu.how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. -action.escu.data_models = ["Network_Resolution"] -action.escu.full_search_name = ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule -action.escu.mappings = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} -action.escu.known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. +action.escu.eli5 = The search looks for CloudTrail events to detect whether any network ACLs have been deleted and gives you values of error messages and error codes (if any), user details, user source IP, the user who initiated this request, and the name of the event. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.full_search_name = ESCU - AWS Network Access Control List Deleted - Rule +action.escu.mappings = {"mitre_attack": ["Persistence"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11"], "nist": ["DE.DP", "DE.AE"]} +action.escu.known_false_positives = It's possible that a user has legitimately deleted a network ACL. action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Network ACL Activity"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = DNS Query Requests Resolved by Unauthorized DNS Servers +action.correlationsearch.label = AWS Network Access Control List Deleted action.notable = 1 -action.notable.param.nes_fields = dest, src -action.notable.param.rule_description = The table represents a list of unauthorized DNS servers interacting with hosts in your network -action.notable.param.rule_title = DNS requests resolved by unauthorized DNS servers +action.notable.param.nes_fields = src, src_user, eventName +action.notable.param.rule_description = AWS network ACL has been deleted by $src_user. +action.notable.param.rule_title = AWS Network ACL deleted by $src_user$ action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network ACL Details from ID\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 40 +action.risk.param._risk_object = src_user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 80 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,src -alert.suppress.period = 28800s +alert.suppress.fields = src_user +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. -dispatch.earliest_time = -70m@m +description = Enforcing network-access controls is one of the defensive mechanisms used by cloud administrators to restrict access to a cloud instance. After the attacker has gained control of the AWS console by compromising an admin account, they can delete a network ACL and gain access to the instance from anywhere. This search will query the CloudTrail logs to detect users deleting network ACLs. +dispatch.earliest_time = -1d@d dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -5674,7 +5534,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` +search = sourcetype=aws:cloudtrail eventName=DeleteNetworkAcl|rename userIdentity.arn as arn | stats count min(_time) as firstTime max(_time) as lastTime values(errorMessage) values(errorCode) values(userAgent) values(userIdentity.*) by src userName arn eventName | `ctime(lastTime)` | `ctime(firstTime)` [ESCU - Get All AWS Activity From Country] action.escu = 0 @@ -5869,54 +5729,51 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | stats count -[ESCU - Schtasks scheduling job on remote system - Rule] +[ESCU - Baseline of Network ACL Activity by ARN] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-09-13 -action.escu.modification_date = 2017-09-15 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-05-21 +action.escu.modification_date = 2018-05-21 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled on a remote host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or malicious executables on remote systems. -action.escu.how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -action.escu.full_search_name = ESCU - Schtasks scheduling job on remote system - Rule -action.escu.mappings = {"mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task", "Remote Services"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} -action.escu.known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Lateral Movement"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Schtasks scheduling job on remote system -action.notable = 1 -action.notable.param.nes_fields = dest, user, process -action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. -action.notable.param.rule_title = Schtasks scheduling job on remote system -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,cmdline -alert.suppress.period = 28800s -cron_schedule = 0 * * * * -description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. -dispatch.earliest_time = -70m@m +action.escu.eli5 = Use this search to create a baseline for API calls related to network ACLs for the users who initiated this activity. It returns all logged API calls for network activity, pulls out the ARN that initiated each call, and collects the `eventNames` in one-hour groupings. Next, it calculates the number of API calls made per ARN per-hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points for each ARN. This table is stored in a lookup file. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. To add or remove API event names for network ACLs, edit the macro `NetworkACLEvents`. +action.escu.full_search_name = ESCU - Baseline of Network ACL Activity by ARN +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Network ACL Activity"] +description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls that were related to network ACLs made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*schtasks.exe* cmdline="*/create*" cmdline="* /s *" | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` +search = sourcetype=aws:cloudtrail `NetworkACLEvents` | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup network_acl_activity_baseline | stats count + +[ESCU - Get Authentication Logs For Endpoint] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-04-10 +action.escu.modification_date = 2017-11-01 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. +action.escu.data_models = ["Authentication"] +action.escu.full_search_name = ESCU - Get Authentication Logs For Endpoint +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["Microsoft Windows", "Linux", "macOS"] +action.escu.analytic_story = ["Suspicious WMI Use", "ColdRoot MacOS RAT", "JBoss Vulnerability", "Data Protection", "Ransomware", "Suspicious MSHTA Activity", "Hidden Cobra Malware", "Windows Log Manipulation", "Suspicious Windows Registry Activities", "Splunk Enterprise Vulnerability", "Windows File Extension and Association Abuse", "Suspicious DNS Traffic", "Windows Persistence Techniques", "Dynamic DNS", "Apache Struts Vulnerability", "Credential Dumping", "Monitor for Updates", "Spectre And Meltdown Vulnerabilities", "Collection and Staging", "Emotet Malware (TA18-201A)", "Monitor for Unauthorized Software", "Suspicious Emails", "SQL Injection", "Windows Service Abuse", "Suspicious Command-Line Executions", "Windows Privilege Escalation", "Asset Tracking", "DHS Report TA18-074A", "Account Monitoring and Controls", "Malicious PowerShell", "Orangeworm Attack Group", "Router & Infrastructure Security", "Brand Monitoring", "Disabling Security Tools", "Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch", "Unusual Processes", "Host Redirection", "Windows Defense Evasion Tactics", "Lateral Movement", "SamSam Ransomware", "Netsh Abuse", "ColdRoot MacOS RAT", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 43200 +action.escu.latest_time_offset = 1 +description = This search returns all users that have attempted to access a particular endpoint. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats count from datamodel=Authentication where Authentication.dest=$dest$ by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action | `drop_dm_object_name("Authentication")` [ESCU - Detect web traffic to dynamic domain providers - Rule] action.escu = 0 @@ -6016,27 +5873,55 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*wevtutil.exe* cmdline="*cl*" (cmdline="*System**" OR cmdline="*Security*" OR cmdline="*Setup*" OR cmdline="*Application*") | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - Previously Seen EC2 AMIs] +[ESCU - Prohibited Network Traffic Allowed - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-12 -action.escu.modification_date = 2018-03-12 +action.escu.creation_date = 2017-04-18 +action.escu.modification_date = 2017-09-11 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -action.escu.full_search_name = ESCU - Previously Seen EC2 AMIs -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining"] -description = This search builds a table of previously seen AMIs used to launch EC2 instances -dispatch.earliest_time = -90d@d +action.escu.confidence = medium +action.escu.eli5 = The search looks for traffic marked 'is_prohibited' in the Enterprise Security lookup table 'interesting_ports_lookup', and then determines if any network devices have an associated 'allow' action on that traffic by checking the Network_Traffic data model. +action.escu.how_to_implement = In order to properly run this search, Splunk needs to ingest data from firewalls or other network control devices that mediate the traffic allowed into an environment. This is necessary so that the search can identify an 'action' taken on the traffic of interest. The search requires the Network_Traffic data model be populated. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Prohibited Network Traffic Allowed - Rule +action.escu.mappings = {"mitre_attack": ["Command and Control", "Commonly Used Port", "Exfiltration", "Exfiltration Over Alternative Protocol"], "kill_chain_phases": ["Delivery", "Command and Control"], "cis20": ["CIS 9", "CIS 12"], "nist": ["DE.AE", "PR.AC"]} +action.escu.known_false_positives = None identified +action.escu.search_type = detection +action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] +action.escu.analytic_story = ["Ransomware", "Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Prohibited Network Traffic Allowed +action.notable = 1 +action.notable.param.nes_fields = src_ip, dest_ip +action.notable.param.rule_description = This search looks for network traffic defined by port and transport in the ES lookup table "lookup_interesting_ports", that is marked as prohibited, and yet has an 'allow' action in the Network_Traffic data model. This should help to identify areas where a network device is not properly configured. +action.notable.param.rule_title = Prohibited Network Traffic Allowed from $src_ip$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Information For Port Activity\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src_ip +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 40 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest_ip,src_ip +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for network traffic defined by port and transport layer protocol in the Enterprise Security lookup table "lookup_interesting_ports", that is marked as prohibited, and has an associated 'allow' action in the Network_Traffic data model. This could be indicative of a misconfigured network device. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId as amiID | stats earliest(_time) as earliest latest(_time) as latest by amiID | outputlookup previously_seen_ec2_amis.csv | stats count +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.action = allowed by All_Traffic.src_ip All_Traffic.dest_ip All_Traffic.dest_port All_Traffic.action | lookup update=true interesting_ports_lookup dest_port as All_Traffic.dest_port OUTPUT app is_prohibited note transport | search is_prohibited=true | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` [ESCU - Detect Rare Executables - Rule] action.escu = 0 @@ -6100,7 +5985,7 @@ action.escu.full_search_name = ESCU - AWS Network Interface details via resource action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS Traffic", "Command and Control", "AWS Network ACL Activity"] +action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command and Control"] action.escu.fields_required = ["resourceId"] action.escu.earliest_time_offset = 86400 action.escu.latest_time_offset = 0 @@ -6161,28 +6046,76 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` values(Web.url) as urls min(_time) as firstTime from datamodel=Web by Web.src | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `brand_abuse_web` -[ESCU - Identify Systems Receiving Remote Desktop Traffic] +[ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-04-24 -action.escu.modification_date = 2017-09-15 +action.escu.creation_date = 2018-03-12 +action.escu.modification_date = 2018-03-12 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU -action.escu.eli5 = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic. -action.escu.how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Identify Systems Receiving Remote Desktop Traffic +action.escu.confidence = medium +action.escu.eli5 = The subsearch returns the instance types of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the earliest seen time field for each instance type and returns only those instance types that has first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. +action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Cryptomining"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = EC2 Instance Started With Previously Unseen Instance Type +action.notable = 1 +action.notable.param.nes_fields = instanceType +action.notable.param.rule_description = The EC2 instance type $instanceType$ was used for the first time to create $dest$. +action.notable.param.rule_title = New EC2 Instance Type $instanceType$ detected +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for EC2 instances being created with previously unseen instance types. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | convert ctime(earliest) ctime(latest) | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType + +[ESCU - Baseline of API Calls per User ARN] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-04-09 +action.escu.modification_date = 2018-04-09 +action.escu.channel = ESCU +action.escu.eli5 = This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each ARN had. This table is then stored in a lookup file. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +action.escu.full_search_name = ESCU - Baseline of API Calls per User ARN action.escu.known_false_positives = None at this time action.escu.search_type = support -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Lateral Movement"] -description = This search counts the numbers of times the system has created remote desktop traffic -dispatch.earliest_time = -30d@d +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS User Monitoring"] +description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. +dispatch.earliest_time = -90d@d dispatch.latest_time = -10m@m disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count +search = sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count [ESCU - Sc.exe Manipulating Windows Services - Rule] action.escu = 0 @@ -6199,7 +6132,7 @@ action.escu.mappings = {"mitre_attack": ["Persistence", "Privilege Escalation", action.escu.known_false_positives = Using sc.exe to manipulate Windows services is uncommon. However, there may be legitimate instances of this behavior. It is important to validate and investigate as appropriate. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Windows Service Abuse", "Orangeworm Attack Group", "Disabling Security Tools", "Windows Persistence Techniques"] +action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Service Abuse", "DHS Report TA18-074A", "Orangeworm Attack Group", "Disabling Security Tools"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Sc.exe Manipulating Windows Services action.notable = 1 @@ -6233,44 +6166,44 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*sc.exe* AND (cmdline="* create *" OR cmdline="* config *") | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - Remote Desktop Network Bruteforce - Rule] +[ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-12-14 -action.escu.modification_date = 2018-12-14 +action.escu.creation_date = 2017-07-08 +action.escu.modification_date = 2017-09-18 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = This search monitors for abnormal amounts of remote-desktop (RDP) traffic from a source to a destination that may be indicative of a brute-force attack. It does this by filtering out RDP traffic from the Network_Traffic.All_Traffic data model, using twice the standard deviation of all source-to-destination connections. If any tuple is within more than two standard deviations of all other usual RDP traffic flows, it is indicative of a brute-force attack. -action.escu.how_to_implement = You must ensure that your network traffic data is populating the Network_Traffic data model. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Remote Desktop Network Bruteforce - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access", "Remote Desktop Protocol", "Lateral Movement"], "kill_chain_phases": ["Reconnaissance", "Delivery"], "cis20": ["CIS 12", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -action.escu.known_false_positives = RDP gateways may have unusually high amounts of traffic from all other hosts' RDP applications in the network. +action.escu.eli5 = Clients should be resolving their DNS requests via a trusted DNS server. This search will identify DNS queries being sent to unauthorized DNS servers by comparing the destination and source of the traffic with assets marked as DNS servers. +action.escu.how_to_implement = To successfully implement this search you will need to ensure that DNS data is populating the Network_Resolution data model. It also requires that your DNS servers are identified correctly in the Assets and Identity table of Enterprise Security. +action.escu.data_models = ["Network_Resolution"] +action.escu.full_search_name = ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule +action.escu.mappings = {"mitre_attack": ["Exfiltration", "Command and Control", "Defense Evasion", "Commonly Used Port"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 1", "CIS 3", "CIS 8", "CIS 12"], "nist": ["ID.AM", "PR.DS", "PR.IP", "DE.AE", "DE.CM"]} +action.escu.known_false_positives = Legitimate DNS activity can be detected in this search. Investigate, verify and update the list of authorized DNS servers as appropriate. action.escu.search_type = detection -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["SamSam Ransomware"] +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote Desktop Network Bruteforce +action.correlationsearch.label = DNS Query Requests Resolved by Unauthorized DNS Servers action.notable = 1 action.notable.param.nes_fields = dest, src -action.notable.param.rule_description = Remote-desktop traffic detected from $src$ to $dest$. This activity is consistent with a brute-force attack. -action.notable.param.rule_title = Bruteforce Remote Desktop Network Traffic detected from $src$ to $dest$ +action.notable.param.rule_description = The table represents a list of unauthorized DNS servers interacting with hosts in your network +action.notable.param.rule_title = DNS requests resolved by unauthorized DNS servers action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src action.risk.param._risk_object_type = system -action.risk.param._risk_score = 75 +action.risk.param._risk_score = 40 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 alert.suppress.fields = dest,src alert.suppress.period = 28800s cron_schedule = 0 * * * * -description = This search looks for RDP application network traffic and filters any source/destination pair generating more than twice the standard deviation of the average traffic. +description = This search will detect DNS requests resolved by unauthorized DNS servers. Legitimate DNS servers should be identified in the Enterprise Security Assets and Identity Framework. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -6281,7 +6214,78 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.app=rdp by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | eventstats stdev(count) AS stdev avg(count) AS avg p50(count) AS p50| where count>(stdev*2) | rename All_Traffic.src AS src All_Traffic.dest AS dest | table firstTime lastTime src dest count avg p50 stdev +search = | tstats `summariesonly` count from datamodel=Network_Resolution where DNS.dest_category != dns_server AND DNS.src_category != dns_server by DNS.src DNS.dest | `drop_dm_object_name("DNS")` + +[ESCU - Detect Spike in S3 Bucket deletion - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-07-17 +action.escu.modification_date = 2018-11-27 +action.escu.asset_at_risk = S3 Bucket +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = This search and its corresponding subsearch run through the following series of steps: \ +\ +1. Retrieve all the AWS CloudTrail log entries that have recorded AWS API calls specifically for deletion of S3 buckets.\ +\ +1. Kick off a subsearch that retrieves the same data and pulls out and converts the ARN into a more friendly format.\ +\ +1. Count the number of API calls per ARN.\ +\ +1. Load the cache file that contains the number of data points, the count from the latest hour, the API call average, and the standard deviation for each ARN.\ +\ +1. Drop the count from the latest hour, since it is unnecessary, and merge the rest of the data with the results of the `stats` command. \ +\ +1. Rename `apiCalls` as `latestCount`.\ +\ +1. Calculate the new average value for each ARN with the latest count, weighting the past more heavily than the current hour. It does the same for the standard deviation—weighting the past more heavily than the current.\ +\ +1. Update the cache file with the latest results.\ +\ +1. Set the minimum threshold for the number of data points and the number of standard deviations away from the mean it must be to be considered a spike.\ +\ +1. Make a determination regarding whether or not the current count is a spike by checking to see if the minimum data-point threshold has been met and if the count is a sufficient number of standard deviations away from the average.\ +\ +1. Filter out anything that it determines is not a spike and returns the list of ARNs to the main search. The main search subsequently gets the names of the deleted S3 buckets, the number of unique API calls, and the total number of API calls for each of these user ARNs. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. You can modify `dataPointThreshold` and `deviationThreshold` to better fit your environment. The `dataPointThreshold` variable is the minimum number of data points required to have a statistically significant amount of data to determine. The `deviationThreshold` variable is the number of standard deviations away from the mean that the value must be to be considered a spike. This search works best when you run the "Baseline of S3 Bucket deletion activity by ARN" support search once to create a baseline of previously seen S3 bucket-deletion activity. +action.escu.full_search_name = ESCU - Detect Spike in S3 Bucket deletion - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access", "Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.CM", "PR.AC"]} +action.escu.known_false_positives = Based on the values of`dataPointThreshold` and `deviationThreshold`, the false positive rate may vary. Please modify this according the your environment. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS S3 Activities"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Spike in S3 Bucket deletion +action.notable = 1 +action.notable.param.nes_fields = user +action.notable.param.rule_description = A spike in the number of S3 buckets deleted by $user$ was detected. +action.notable.param.rule_title = Spike detected in S3 bucket deletion activity by $user$. +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = user +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search detects users creating spikes in API activity related to deletion of S3 buckets in your AWS environment. It will also update the cache file that factors in the latest data. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail eventName=DeleteBucket [search sourcetype=aws:cloudtrail eventName=DeleteBucket | spath output=arn path=userIdentity.arn | stats count as apiCalls by arn | inputlookup s3_deletion_baseline append=t | fields - latestCount | stats values(*) as * by arn | rename apiCalls as latestCount | eval newAvgApiCalls=avgApiCalls + (latestCount-avgApiCalls)/720 | eval newStdevApiCalls=sqrt(((pow(stdevApiCalls, 2)*719 + (latestCount-newAvgApiCalls)*(latestCount-avgApiCalls))/720)) | eval avgApiCalls=coalesce(newAvgApiCalls, avgApiCalls), stdevApiCalls=coalesce(newStdevApiCalls, stdevApiCalls), numDataPoints=if(isnull(latestCount), numDataPoints, numDataPoints+1) | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup s3_deletion_baseline | eval dataPointThreshold = 15, deviationThreshold = 3 | eval isSpike=if((latestCount > avgApiCalls+deviationThreshold*stdevApiCalls) AND numDataPoints > dataPointThreshold, 1, 0) | where isSpike=1 | rename arn as userIdentity.arn | table userIdentity.arn] | spath output=user userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | stats values(bucketName) as bucketName, count as numberOfApiCalls, dc(eventName) as uniqueApisCalled by user [ESCU - Deleting Shadow Copies - Rule] action.escu = 0 @@ -6299,7 +6303,7 @@ action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Ac action.escu.known_false_positives = vssadmin.exe and wmic.exe are standard applications shipped with modern versions of windows. They may be used by administrators to legitimately delete old backup copies, although this is typically rare. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "SamSam Ransomware", "Windows Log Manipulation"] +action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation", "SamSam Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Deleting Shadow Copies action.notable = 1 @@ -6308,7 +6312,7 @@ action.notable.param.rule_description = Using $process_name$ to delete shadow co action.notable.param.rule_title = Deleting Shadow Copies on $dest$ with $process_name$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -6368,7 +6372,7 @@ action.escu.full_search_name = ESCU - AWS Investigate User Activities By ARN action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities", "Suspicious AWS EC2 Activities", "Unusual AWS EC2 Modifications", "Suspicious AWS Login Activities", "AWS Network ACL Activity"] +action.escu.analytic_story = ["Unusual AWS EC2 Modifications", "AWS Network ACL Activity", "Suspicious AWS S3 Activities", "Suspicious AWS EC2 Activities", "Suspicious AWS Login Activities"] action.escu.fields_required = ["arn"] action.escu.earliest_time_offset = 14400 action.escu.latest_time_offset = 0 @@ -6428,54 +6432,28 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*schtasks.exe shutdown.exe | search (cmdline=*/r* AND cmdline=*/f*) | stats count values(cmdline) min(_time) as firstTime max(_time) as lastTime by dest process | `ctime(firstTime)` | `ctime(lastTime)` -[ESCU - Suspicious writes to windows Recycle Bin - Rule] +[ESCU - Count of assets by category] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-01-08 -action.escu.modification_date = 2018-01-08 -action.escu.asset_at_risk = Windows +action.escu.creation_date = 2017-06-11 +action.escu.modification_date = 2017-09-13 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search uses data on file writes captured via Sysmon to watch for writes to the Recycle Bin by processes other than explorer.exe. The search looks for event code 11 in the Sysmon events, which indicates a file-creation event. Next, it looks for files created with a path that includes the string "$Recycle.Bin" by processes other than explorer.exe, which is the process responsible for copying files to the Recycle Bin on delete. It will report the system where the activity occurred, the path to which the file was written, the process responsible for the write, and the times it first and last saw this activity. -action.escu.how_to_implement = You need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. -action.escu.full_search_name = ESCU - Suspicious writes to windows Recycle Bin - Rule -action.escu.mappings = {"mitre_attack": ["Collection", "Data Staged"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -action.escu.known_false_positives = Because the Recycle Bin is a hidden folder in modern versions of Windows, it would be unusual for a process other than explorer.exe to write to it. Incidents should be investigated as appropriate. -action.escu.search_type = detection -action.escu.providing_technologies = ["Sysmon"] -action.escu.analytic_story = ["Collection and Staging"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Suspicious writes to windows Recycle Bin -action.notable = 1 -action.notable.param.nes_fields = dest, file_name, process -action.notable.param.rule_description = The process $process$ on $dest$ wrote $file_name$ to the Recycle Bin. -action.notable.param.rule_title = Suspicious process $process$ wrote to the Recycle Bin on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = This search detects writes to the recycle bin by a process other than explorer.exe. -dispatch.earliest_time = -70m@m +action.escu.eli5 = This search gives you the number and the names of the hosts of each host in your environment by category. It will then sort them by the count. +action.escu.how_to_implement = To successfully implement this search you must first leverage the Assets and Identity framework in Enterprise Security to populate your assets_by_str.csv file which should then be mapped to the Identity_Management data model. The Identity_Management data model will contain a list of known authorized company assets. Ensure that all inventoried systems are constantly vetted and updated. +action.escu.data_models = ["Identity_Management"] +action.escu.full_search_name = ESCU - Count of assets by category +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["Splunk Enterprise Security"] +action.escu.analytic_story = ["Asset Tracking"] +description = This search shows you every asset category you have and the assets that belong to those categories. +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) EventCode=11 file_path=*$Recycle.Bin* process!=explorer.exe | stats count min(_time) as firstTime max(_time) as lastTime by dest, Image, file_path | `ctime(firstTime)`| `ctime(lastTime)` +search = | from datamodel Identity_Management.All_Assets | stats count values(nt_host) by category | sort -count [ESCU - Get Email Info] action.escu = 0 @@ -6490,7 +6468,7 @@ action.escu.full_search_name = ESCU - Get Email Info action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"] +action.escu.analytic_story = ["Suspicious Emails", "Brand Monitoring"] action.escu.fields_required = ["message_id"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 7200 @@ -6513,7 +6491,7 @@ action.escu.full_search_name = ESCU - Monitor Unsuccessful Backups action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["Ransomware", "SamSam Ransomware", "Monitor Backup Solution"] +action.escu.analytic_story = ["Ransomware", "Monitor Backup Solution", "SamSam Ransomware"] description = This search is intended to give you a feel for how often backup failures happen in your environments. Fluctuations in these numbers will allow you to determine when you should investigate. dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m @@ -6545,6 +6523,105 @@ schedule_window = auto is_visible = false search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats count +[ESCU - Common Ransomware Extensions - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-08-21 +action.escu.modification_date = 2018-11-15 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = high +action.escu.eli5 = This search looks at file modifications across your hosts and identifies files with extensions that are commonly associated with the encrypted files generated by ransomware. +action.escu.how_to_implement = You must be ingesting data that records the filesystem activity from your hosts to populate the Endpoint file-system data model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Common Ransomware Extensions - Rule +action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.known_false_positives = It is possible for a legitimate file with these extensions to be created. If this is a true ransomware attack, there will be a large number of files created with these extensions. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Common Ransomware Extensions +action.notable = 1 +action.notable.param.nes_fields = dest, file_name +action.notable.param.rule_description = A file modification was detected on $dest$ with an extension commonly used by ransomware. +action.notable.param.rule_title = Ransomware Extension detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 80 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,file_name +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = The search looks for file modifications with extensions commonly used by Ransomware +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.user) as user values(Filesystem.dest) as dest values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | `ransomware_extensions` + +[ESCU - Abnormally High AWS Instances Terminated by User - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-02-26 +action.escu.modification_date = 2018-02-26 +action.escu.asset_at_risk = AWS Instance +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = In this search, we query CloudTrail logs to look for events where an instance is successfully terminated by a particular user. Since we want to detect a high number of instances terminated within a short period, we create event buckets for 10-minute windows. We then calculate the total number of instances terminated by a particular user, as well as the average- and standard-deviation values. Assign a `threshold_value` in the search. Try starting with 3 (but it will likely need to be tweaked for your environment). The `eval` function will set the outlier to 1 if the number of instances is greater than the average number of instances terminated, added to the multiplied value of threshold and standard deviation. We then filter out outliers with a value of 1 and show only those instance-termination events that happened within the previous 10 minutes. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. +action.escu.full_search_name = ESCU - Abnormally High AWS Instances Terminated by User - Rule +action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["DE.DP", "DE.AE"]} +action.escu.known_false_positives = Many service accounts configured with your AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify whether this search alerted on a human user. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["Suspicious AWS EC2 Activities"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Abnormally High AWS Instances Terminated by User +action.notable = 1 +action.notable.param.nes_fields = userName +action.notable.param.rule_description = An abnormally high number of instances were terminated by a user in a 10-minute window +action.notable.param.rule_title = High number of instances terminated by $userName$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Instance Details by instanceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Investigate AWS activities via region name\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = userName +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = userName +alert.suppress.period = 3600s +cron_schedule = */10 * * * * +description = This search looks for CloudTrail events where an abnormally high number of instances were successfully terminated by a user in a 10-minute window +dispatch.earliest_time = -30d@d +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail eventName=TerminateInstances errorCode=success | bucket span=10m _time | stats count AS instances_terminated by _time userName | eventstats avg(instances_terminated) as total_terminations_avg, stdev(instances_terminated) as total_terminations_stdev | eval threshold_value = 4 | eval isOutlier=if(instances_terminated > total_terminations_avg+(total_terminations_stdev * threshold_value), 1, 0) | search isOutlier=1 AND _time >= relative_time(now(), "-10m@m")| eval num_standard_deviations_away = round(abs(instances_terminated - total_terminations_avg) / total_terminations_stdev, 2) |table _time, userName, instances_terminated, num_standard_deviations_away, total_terminations_avg, total_terminations_stdev + [ESCU - Extended Period Without Successful Netbackup Backups - Rule] action.escu = 0 action.escu.enabled = 1 @@ -6681,7 +6758,7 @@ action.escu.mappings = {"mitre_attack": ["Persistence", "Privilege Escalation", action.escu.known_false_positives = It is unusual for a service to be created or modified by directly manipulating the registry. However, there may be legitimate instances of this behavior. It is important to validate and investigate, as appropriate. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Service Abuse", "Windows Persistence Techniques"] +action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Service Abuse"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Reg.exe Manipulating Windows Services Registry Keys action.notable = 1 @@ -6865,6 +6942,56 @@ schedule_window = auto is_visible = false search = sourcetype="stream:http" http_method=POST http_content_length>1 | regex form_data="(?i)java\.lang\.(?:runtime|processbuilder)" | rename src_ip as src | stats count earliest(_time) as firstTime, latest(_time) as lastTime, values(url) as uri, values(status) as status, values(http_user_agent) as http_user_agent by src, dest | convert ctime(firstTime) ctime(lastTime) +[ESCU - Registry Keys for Creating SHIM Databases - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-08-27 +action.escu.modification_date = 2017-09-15 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor. +action.escu.how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. +action.escu.data_models = ["Change_Analysis"] +action.escu.full_search_name = ESCU - Registry Keys for Creating SHIM Databases - Rule +action.escu.mappings = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Registry Keys for Creating SHIM Databases +action.notable = 1 +action.notable.param.nes_fields = dest, user +action.notable.param.rule_description = A registry key that is used for persistence on Windows was modified on $dest$ by $user$ +action.notable.param.rule_title = Registry Key Associated With SHIM databases on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 30 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,object_path +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\Custom*" OR All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\InstalledSDB*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `drop_dm_object_name("All_Changes")` + [ESCU - Web Fraud - Account Harvesting - Rule] action.escu = 0 action.escu.enabled = 1 @@ -6927,7 +7054,7 @@ action.escu.full_search_name = ESCU - Investigate Web Activity From Host action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.analytic_story = ["Credential Dumping", "Suspicious Command-Line Executions", "Host Redirection", "Ransomware", "Orangeworm Attack Group", "Emotet Malware (TA18-201A)", "Unusual Processes", "Brand Monitoring", "SamSam Ransomware", "Monitor for Unauthorized Software", "Netsh Abuse", "Suspicious Emails"] +action.escu.analytic_story = ["Ransomware", "Credential Dumping", "Emotet Malware (TA18-201A)", "Monitor for Unauthorized Software", "Suspicious Emails", "Suspicious Command-Line Executions", "Orangeworm Attack Group", "Brand Monitoring", "Unusual Processes", "Host Redirection", "SamSam Ransomware", "Netsh Abuse"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 3600 @@ -6938,102 +7065,6 @@ schedule_window = auto is_visible = false search = | from datamodel Web.Web | search src=$dest$ -[ESCU - Get All AWS Activity From City] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-03-19 -action.escu.modification_date = 2018-03-19 -action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. -action.escu.full_search_name = ESCU - Get All AWS Activity From City -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -action.escu.fields_required = ["City"] -action.escu.earliest_time_offset = 14400 -action.escu.latest_time_offset = 0 -description = This search retrieves all the activity from a specific city and will create a table containing the time, city, ARN, username, the type of user, the source IP address, the AWS region the activity was in, the API called, and whether or not the API call was successful. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search City=$City$ | spath output=user path=userIdentity.arn | spath output=awsUserName path=userIdentity.userName | spath output=userType path=userIdentity.type | rename sourceIPAddress as src_ip | table _time, City, user, userName, userType, src_ip, awsRegion, eventName, errorCode - -[ESCU - Malicious PowerShell Process - Encoded Command - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2016-09-18 -action.escu.modification_date = 2018-12-03 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search looks for PowerShell processes that are passing encoded commands on the command-line. The flags "-EncodedCommand" and "-enc" are two different possible flags that can be used to pass base64 encoded commands to PowerShell. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Malicious PowerShell Process - Encoded Command - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -action.escu.known_false_positives = System administrators may use this option, but it's not common. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Malicious PowerShell"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process - Encoded Command -action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = The system $dest$ executed a PowerShell process that has an encoded command on the command-line -action.notable.param.rule_title = PowerShell process with an encoded command detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user, process_name -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search process=*-EncodedCommand* OR process=*-enc* - -[ESCU - Add Prohibited Processes to Enterprise Security] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-06-27 -action.escu.modification_date = 2017-09-15 -action.escu.channel = ESCU -action.escu.eli5 = This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries. -action.escu.how_to_implement = This search should be run on each new install of ESCU. -action.escu.data_models = [] -action.escu.full_search_name = ESCU - Add Prohibited Processes to Enterprise Security -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["SamSam Ransomware", "Monitor for Unauthorized Software"] -description = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain | fillnull value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup interesting_processes_lookup | stats count - [ESCU - Monitor Email For Brand Abuse - Rule] action.escu = 0 action.escu.enabled = 1 @@ -7107,29 +7138,54 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` dc(Updates.dest) as count FROM datamodel=Updates where Updates.vendor_product="Microsoft Windows" AND Updates.status=failure by _time span=1d -[ESCU - Get Authentication Logs For Endpoint] +[ESCU - Schtasks scheduling job on remote system - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-04-10 -action.escu.modification_date = 2017-11-01 +action.escu.creation_date = 2016-09-13 +action.escu.modification_date = 2017-09-15 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you need to be ingesting authentication logs from your various systems and populating the Authentication data model. -action.escu.data_models = ["Authentication"] -action.escu.full_search_name = ESCU - Get Authentication Logs For Endpoint -action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["Microsoft Windows", "Linux", "macOS"] -action.escu.analytic_story = ["Apache Struts Vulnerability", "Credential Dumping", "Suspicious Windows Registry Activities", "DHS Report TA18-074A", "Suspicious Command-Line Executions", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Host Redirection", "Collection and Staging", "Ransomware", "Windows Service Abuse", "Command and Control", "ColdRoot MacOS RAT", "Orangeworm Attack Group", "SQL Injection", "Emotet Malware (TA18-201A)", "Disabling Security Tools", "Suspicious WMI Use", "Prohibited Traffic Allowed or Protocol Mismatch", "Hidden Cobra Malware", "Unusual Processes", "Data Protection", "Brand Monitoring", "Asset Tracking", "Account Monitoring and Controls", "Windows Privilege Escalation", "ColdRoot MacOS RAT", "SamSam Ransomware", "Monitor for Unauthorized Software", "Windows Persistence Techniques", "Malicious PowerShell", "Spectre And Meltdown Vulnerabilities", "Dynamic DNS", "Lateral Movement", "Suspicious DNS Traffic", "Splunk Enterprise Vulnerability", "Netsh Abuse", "Windows Log Manipulation", "Router & Infrastructure Security", "Suspicious Emails", "Suspicious MSHTA Activity", "JBoss Vulnerability", "Monitor for Updates", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 43200 -action.escu.latest_time_offset = 1 -description = This search returns all users that have attempted to access a particular endpoint. +action.escu.confidence = medium +action.escu.eli5 = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled on a remote host. Schtasks.exe is a native windows program that is used to schedule tasks on local or remote systems. Attackers often leverage this capability to schedule the execution of commands or malicious executables on remote systems. +action.escu.how_to_implement = To successfully implement this search you need to be ingesting logs with both the process name and command-line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. +action.escu.full_search_name = ESCU - Schtasks scheduling job on remote system - Rule +action.escu.mappings = {"mitre_attack": ["Persistence", "Lateral Movement", "Execution", "Scheduled Task", "Remote Services"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} +action.escu.known_false_positives = Administrators may create jobs on remote systems, but this activity is usually limited to a small set of hosts or users. It is important to validate and investigate as appropriate. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Lateral Movement"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Schtasks scheduling job on remote system +action.notable = 1 +action.notable.param.nes_fields = dest, user, process +action.notable.param.rule_description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. +action.notable.param.rule_title = Schtasks scheduling job on remote system +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,cmdline +alert.suppress.period = 28800s +cron_schedule = 0 * * * * +description = This search looks for flags passed to schtasks.exe on the command-line that indicate a job is being scheduled on a remote system. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats count from datamodel=Authentication where Authentication.dest=$dest$ by _time, Authentication.dest, Authentication.user, Authentication.app, Authentication.action | `drop_dm_object_name("Authentication")` +search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*schtasks.exe* cmdline="*/create*" cmdline="* /s *" | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` [ESCU - Single Letter Process On Endpoint - Rule] action.escu = 0 @@ -7181,6 +7237,28 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count, latest(_time) as lastTime, earliest(_time) as firstTime from datamodel=Application_State by All_Application_State.dest, All_Application_State.user, All_Application_State.process, All_Application_State.process_name | `drop_dm_object_name("All_Application_State")` | `ctime(lastTime)` | `ctime(firstTime)` | eval process_name_length = len(process_name), endExe = if(substr(process_name, -4) == ".exe", 1, 0) | search process_name_length=5 AND endExe=1 | table count, firstTime, lastTime, dest, user, process, process_name +[ESCU - Previously Seen EC2 AMIs] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-03-12 +action.escu.modification_date = 2018-03-12 +action.escu.channel = ESCU +action.escu.eli5 = In this support search, we create a table of the earliest and latest time that a specific AMI ID has been seen. This table is then outputted to a csv file. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. +action.escu.full_search_name = ESCU - Previously Seen EC2 AMIs +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Cryptomining"] +description = This search builds a table of previously seen AMIs used to launch EC2 instances +dispatch.earliest_time = -90d@d +dispatch.latest_time = -10m@m +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename requestParameters.instancesSet.items{}.imageId as amiID | stats earliest(_time) as earliest latest(_time) as latest by amiID | outputlookup previously_seen_ec2_amis.csv | stats count + [ESCU - Protocols passing authentication in cleartext - Rule] action.escu = 0 action.escu.enabled = 1 @@ -7231,6 +7309,29 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.protocol="tcp" AND (All_Traffic.dest_port="23" OR All_Traffic.dest_port="143" OR All_Traffic.dest_port="110" OR (All_Traffic.dest_port="21" AND All_Traffic.user != "anonymous")) groupby All_Traffic.user All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Traffic")` +[ESCU - Get Web Session Information via session_id] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-10-08 +action.escu.modification_date = 2018-10-08 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = This search leverages data extracted from Stream:HTTP. You must configure the HTTP stream using the Splunk Stream App on your Splunk Stream deployment server. +action.escu.full_search_name = ESCU - Get Web Session Information via session_id +action.escu.known_false_positives = None at this time +action.escu.search_type = investigative +action.escu.providing_technologies = ["Splunk Stream"] +action.escu.analytic_story = ["Web Fraud Detection"] +action.escu.fields_required = ["session_id"] +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 3600 +description = This search helps an analyst investigate a notable event to find out more about a specific web session. The search looks for a specific web session ID in the HTTP web traffic and outputs the URL and user agents, grouped by source IP address and HTTP status code. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | search sourcetype=stream:http $session_id$ | stats values(url) values(http_user_agent) by src_ip status + [ESCU - Processes created by netsh - Rule] action.escu = 0 action.escu.enabled = 1 @@ -7281,29 +7382,28 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Processes.process) min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process="C:\Windows\System32\netsh.exe" by Processes.parent_process Processes.process_name Processes.user Processes.dest | `drop_dm_object_name("Processes")` | `ctime(firstTime)`|`ctime(lastTime)` -[ESCU - Get First Occurrence and Last Occurrence of a MAC Address] +[ESCU - Add Prohibited Processes to Enterprise Security] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-06-14 -action.escu.modification_date = 2017-09-13 +action.escu.creation_date = 2017-06-27 +action.escu.modification_date = 2017-09-15 action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. -action.escu.data_models = ["Network_Sessions"] -action.escu.full_search_name = ESCU - Get First Occurrence and Last Occurrence of a MAC Address +action.escu.eli5 = This search outputs the interesting processes lookup table and filters out all processes in the table that haven't already been inserted by ESCU. It then appends to those results all the processes currently identified by ESCU that should be prohibited. Next, it fills in the required fields with processes identified by ESCU, and then writes the results back to the interesting process lookup table. This is done so any new processes identified that should be prohibited will be added to the lookup table without creating any duplicate entries. +action.escu.how_to_implement = This search should be run on each new install of ESCU. +action.escu.data_models = [] +action.escu.full_search_name = ESCU - Add Prohibited Processes to Enterprise Security action.escu.known_false_positives = None at this time -action.escu.search_type = contextual -action.escu.providing_technologies = ["Splunk Stream", "Bro", "Microsoft Windows"] -action.escu.analytic_story = ["Asset Tracking"] -action.escu.fields_required = ["src_mac"] -action.escu.earliest_time_offset = 864000 -action.escu.latest_time_offset = 86400 -description = This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network. +action.escu.search_type = support +action.escu.providing_technologies = ["Splunk Enterprise Security"] +action.escu.analytic_story = ["Monitor for Unauthorized Software", "SamSam Ransomware"] +description = This search takes the existing interesting process table from ES, filters out any existing additions added by ESCU and then updates the table with processes identified by ESCU that should be prohibited on your endpoints. +dispatch.earliest_time = -30d@d +dispatch.latest_time = -10m@m disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats allow_old_summaries=true count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST All_Sessions.All_Sessions.src_mac= $src_mac$ by All_Sessions.src_ip All_Sessions.user | `ctime(lastTime)` | `ctime(firstTime)` +search = | inputlookup interesting_processes_lookup | search note!=ESCU* | inputlookup append=T prohibitedProcesses_lookup | fillnull value=* dest dest_pci_domain | fillnull value=false is_required is_secure | fillnull value=true is_prohibited | outputlookup interesting_processes_lookup | stats count [ESCU - Monitor DNS For Brand Abuse - Rule] action.escu = 0 @@ -7355,55 +7455,29 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` values(DNS.answer) as IPs min(_time) as firstTime from datamodel=Network_Resolution by DNS.src, DNS.query | `drop_dm_object_name("DNS")` | `ctime(firstTime)`| `brand_abuse_dns` -[ESCU - Remote Desktop Network Traffic - Rule] +[ESCU - Get First Occurrence and Last Occurrence of a MAC Address] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-09-13 -action.escu.modification_date = 2017-09-15 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2017-06-14 +action.escu.modification_date = 2017-09-13 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search finds systems that do not commonly communicate use remote desktop. It does this by filtering out all systems that have the "common_rdp_source" or "common_rdp_destination" category applied to that system. Categories are applied to systems using the Assets and Identity framework. -action.escu.how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Remote Desktop Network Traffic - Rule -action.escu.mappings = {"mitre_attack": ["Lateral Movement", "Remote Desktop Protocol", "Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} -action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network. -action.escu.search_type = detection -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["Hidden Cobra Malware", "SamSam Ransomware", "Lateral Movement"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Remote Desktop Network Traffic -action.notable = 1 -action.notable.param.nes_fields = dest, src -action.notable.param.rule_description = Remote Desktop Traffic detected between $src$ and $dest$. These two systems typically do not communicate with RDP -action.notable.param.rule_title = Uncommon Remote Desktop Network Traffic between $src$ and $dest$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,src -alert.suppress.period = 28800s -cron_schedule = 0 * * * * -description = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search allows for whitelisting both source and destination hosts to remove them from the output of the search so you can focus on the uncommon uses of remote desktop on your network. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m +action.escu.eli5 = none +action.escu.how_to_implement = To successfully implement this search, you must be ingesting the logs from your DHCP server. +action.escu.data_models = ["Network_Sessions"] +action.escu.full_search_name = ESCU - Get First Occurrence and Last Occurrence of a MAC Address +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["Splunk Stream", "Bro", "Microsoft Windows"] +action.escu.analytic_story = ["Asset Tracking"] +action.escu.fields_required = ["src_mac"] +action.escu.earliest_time_offset = 864000 +action.escu.latest_time_offset = 86400 +description = This search allows you to gather more context around a notable which has detected a new device connecting to your network. Use this search to determine the first and last occurrences of the suspicious device attempting to connect with your network. disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `ctime(firstTime)`| `ctime(lastTime)` +search = | tstats allow_old_summaries=true count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Sessions where nodename=All_Sessions.DHCP All_Sessions.signature=DHCPREQUEST All_Sessions.All_Sessions.src_mac= $src_mac$ by All_Sessions.src_ip All_Sessions.user | `ctime(lastTime)` | `ctime(firstTime)` [ESCU - Create local admin accounts using net.exe - Rule] action.escu = 0 @@ -7600,27 +7674,55 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=reg.exe by Processes.user Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search process=*save* (process=*HKLM\\sam* OR process=*HKLM\\system*) -[ESCU - Baseline of API Calls per User ARN] +[ESCU - Malicious PowerShell Process - Encoded Command - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-04-09 -action.escu.modification_date = 2018-04-09 +action.escu.creation_date = 2016-09-18 +action.escu.modification_date = 2018-12-03 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = This search returns all log events that are API calls, pulls out the ARN that initiated each call, and collects them in one-hour groupings. Next, it calculates the number of API calls made per ARN per hour. For each ARN, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each ARN had. This table is then stored in a lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -action.escu.full_search_name = ESCU - Baseline of API Calls per User ARN -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS User Monitoring"] -description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of API calls made by each user. Also recorded is the number of data points for each user. This table is then outputted to a lookup file to allow the detection search to operate quickly. -dispatch.earliest_time = -90d@d +action.escu.confidence = medium +action.escu.eli5 = This search looks for PowerShell processes that are passing encoded commands on the command-line. The flags "-EncodedCommand" and "-enc" are two different possible flags that can be used to pass base64 encoded commands to PowerShell. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Malicious PowerShell Process - Encoded Command - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.known_false_positives = System administrators may use this option, but it's not common. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Malicious PowerShell"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Malicious PowerShell Process - Encoded Command +action.notable = 1 +action.notable.param.nes_fields = dest, user, process_name +action.notable.param.rule_description = The system $dest$ executed a PowerShell process that has an encoded command on the command-line +action.notable.param.rule_title = PowerShell process with an encoded command detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 20 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest, user, process_name +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for PowerShell processes that have encoded the script within the command-line. Malware has been seen using this parameter, as it obfuscates the code and makes it relatively easy to pass a script on the command-line. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail eventType=AwsApiCall | spath output=arn path=userIdentity.arn | bucket _time span=1h | stats count as apiCalls by _time, arn | stats count(apiCalls) as numDataPoints, latest(apiCalls) as latestCount, avg(apiCalls) as avgApiCalls, stdev(apiCalls) as stdevApiCalls by arn | table arn, latestCount, numDataPoints, avgApiCalls, stdevApiCalls | outputlookup api_call_by_user_baseline | stats count +search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` | search process=*-EncodedCommand* OR process=*-enc* [ESCU - Get EC2 Launch Details] action.escu = 0 @@ -7645,44 +7747,61 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:cloudtrail responseElements.instancesSet.items{}.instanceId=$dest$ |rename userIdentity.arn as arn, responseElements.instancesSet.items{}.instanceId as instanceId, responseElements.instancesSet.items{}.privateIpAddress as privateIpAddress, responseElements.instancesSet.items{}.imageId as amiID, responseElements.instancesSet.items{}.architecture as architecture, responseElements.instancesSet.items{}.keyName as keyName | table arn, awsRegion, instanceId, architecture, privateIpAddress, amiID, keyName -[ESCU - Attempt To Add Certificate To Untrusted Store - Rule] +[ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-04-09 -action.escu.modification_date = 2018-11-15 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-02-01 +action.escu.modification_date = 2018-11-02 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = Attackers will often attempt to disable security tools in order to evade detection. It is also possible for end users to attempt to disable anti-virus or other security tools to circumvent restrictions they encounter while trying to execute other programs. One way malware may accomplish this is by adding the legitimate certificate used to sign the security software to the untrusted certificate store. This will cause the system to no longer trust the software signed with this certificate and disallow it from executing. This search simply looks for the execution of **certutil.exe** with the parameters `-addcert` and `disallowed`, which add a certification to the "untrusted" certificate store. -action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Attempt To Add Certificate To Untrusted Store - Rule -action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -action.escu.known_false_positives = There may be legitimate reasons for administrators to add a certificate to the untrusted certificate store. In such cases, this will typically be done on a large number of systems. +action.escu.confidence = medium +action.escu.eli5 = This search\ +\ +1. Retrieves the **AssumeRole** event\ +\ +1. Verifies that the log entry contains a value for the account ID of the requesting account\ +\ +1. Ensures that the requesting account ID does not match the account ID of the requested account\ +\ +1. Pulls in the previously seen requesting and requested account IDs\ +\ +1. Splits up and executes multiple search paths at the same.\ +\ +1. The first path determines the **firstTime** and **lastTime** entries for the cache file\ +\ +1. Outputs the data to the cache file.\ +\ +1. Creates a conditional statement that is always false (both because we don't want these values to exit the search pipeline and because we think we're clever).The second pipeline adds the **firstTime** and **lastTime** entries to search results. Next, it filters out any account pairs that haven't been seen for the first time within the last hour. The `isnotnull(_time)` will remove the entries from the cache file.\ +\ +The search finishes by gathering the data that it will display to the user. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search. +action.escu.full_search_name = ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule +action.escu.mappings = {"mitre_attack": ["Credential Access"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} +action.escu.known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Disabling Security Tools"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Cross Account Activity"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Attempt To Add Certificate To Untrusted Store +action.correlationsearch.label = AWS Cross Account Activity From Previously Unseen Account action.notable = 1 -action.notable.param.nes_fields = dest, user, process_name -action.notable.param.rule_description = Attempt to add a certificate to the untrusted certificate store -action.notable.param.rule_title = Attempt To Add Certificate to Untrusted Store -action.notable.param.security_domain = endpoint -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.nes_fields = requestingAccountId, requestedAccountId, src_user, dest_user +action.notable.param.rule_description = Access to $dest_user$ was requested for the first time by $src_user$ +action.notable.param.rule_title = AWS Account $dest_user$ access by $src_user$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By AccessKeyId\n - ESCU - AWS Investigate User Activities By Source User\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 +action.risk.param._risk_object = dest_user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 20 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = process, dest -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = Attempt to add a certificate to the untrusted certificate store +alert.suppress.fields = requestingAccountId, requestedAccountId +alert.suppress.period = 14400s +cron_schedule = 5 * * * * +description = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -7693,79 +7812,52 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) values(Processes.process) as process max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=certutil.exe (Processes.process=*-addstore* AND Processes.process=*disallowed* ) by Processes.parent_process Processes.process_name Processes.user | `drop_dm_object_name("Processes")` | `ctime(firstTime)`|`ctime(lastTime)` +search = sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId != requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | multireport [| stats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime, max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | where firstTime >= relative_time(now(), "-70m@m") AND isnotnull(_time) | spath output=accessKeyId path=responseElements.credentials.accessKeyId | spath output=requestingARN path=resources{}.ARN | stats values(awsRegion) as awsRegion values(firstTime) as firstTime values(lastTime) as lastTime values(sharedEventID) as sharedEventID, values(requestingARN) as src_user, values(responseElements.assumedRoleUser.arn) as dest_user by _time, requestingAccountId, requestedAccountId, accessKeyId] | table _time, firstTime, lastTime, src_user, requestingAccountId, dest_user, requestedAccountId, awsRegion, accessKeyId, sharedEventID -[ESCU - Previously Seen AWS Regions] +[ESCU - Get Backup Logs For Endpoint] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-01-08 -action.escu.modification_date = 2018-01-08 +action.escu.creation_date = 2017-08-24 +action.escu.modification_date = 2017-09-14 action.escu.channel = ESCU -action.escu.eli5 = In this support search, we create a table of the first time (earliest) and most recent time (latest) that this region has been seen in our dataset, grouped by the value `awsRegion`. We only look for those events where an instance has been started. All of these entries will be added to the `previously_seen_aws_regions.csv` lookup file, which will act like a baseline for detections. Please validate the entries of region names in the lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your CloudTrail inputs. -action.escu.full_search_name = ESCU - Previously Seen AWS Regions +action.escu.eli5 = none +action.escu.how_to_implement = You must be ingesting your backup logs. +action.escu.full_search_name = ESCU - Get Backup Logs For Endpoint +action.escu.known_false_positives = None at this time +action.escu.search_type = contextual +action.escu.providing_technologies = ["Netbackup"] +action.escu.analytic_story = ["Ransomware", "SamSam Ransomware"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 604800 +action.escu.latest_time_offset = 0 +description = This search will tell you the backup status from your netbackup_logs of a specific endpoint for the last week. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | search sourcetype="netbackup_logs" COMPUTERNAME=$dest$ | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature + +[ESCU - Baseline of blocked outbound traffic from AWS] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-04-26 +action.escu.modification_date = 2018-05-07 +action.escu.channel = ESCU +action.escu.eli5 = Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each source IP had. This table is then stored in a lookup file. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`. +action.escu.full_search_name = ESCU - Baseline of blocked outbound traffic from AWS action.escu.known_false_positives = None at this time action.escu.search_type = support action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] -description = This search looks for CloudTrail events where an AWS instance is started and creates a baseline of most recent time (latest) and the first time (earliest) we've seen this region in our dataset grouped by the value awsRegion for the last 30 days +action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS Traffic", "Command and Control"] +description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly. dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | stats count - -[ESCU - Email Attachments With Lots Of Spaces - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-04-21 -action.escu.modification_date = 2017-09-19 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search looks at any emails with file attachment names that contain many spaces relative to the length of the file name. Specifically, it checks if spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will then output the message ID of the email, the count, the recipient address and the recipient user, first and last time this event was seen and the space ratio of the file attachment name. -action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. -action.escu.data_models = ["Email"] -action.escu.full_search_name = ESCU - Email Attachments With Lots Of Spaces - Rule -action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 7"], "nist": ["PR.IP"]} -action.escu.known_false_positives = None at this time -action.escu.search_type = detection -action.escu.providing_technologies = ["Microsoft Exchange"] -action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "Suspicious Emails"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Email Attachments With Lots Of Spaces -action.notable = 1 -action.notable.param.nes_fields = src_user, file_name -action.notable.param.rule_description = The sender $src_user$ has sent an email with a suspicious amount of spaces in the file name: $file_name$ -action.notable.param.rule_title = Suspicious Email Attachment from $src_user$ -action.notable.param.security_domain = network -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Email Info\n - ESCU - Get Emails From Specific Sender\n - ESCU - Investigate Web Activity From Host\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 60 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_user -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the filename. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" +search = sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip | stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | stats count [ESCU - Previously seen users in CloudTrail] action.escu = 0 @@ -7812,55 +7904,6 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:cloudtrail userIdentity.arn=$src_user$ | spath output=user path=userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, awsRegion, eventName, errorCode, errorMessage -[ESCU - Detect New Open S3 buckets - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-07-25 -action.escu.modification_date = 2018-07-25 -action.escu.asset_at_risk = S3 Bucket -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search queries CloudTrail logs for events with S3 bucket access controls given to the "All Users" group, which allows anyone in the world access to the resource. This search generates a table displaying the time when the bucket was made public, the permission of the S3 bucket, the bucket name, and the ARN of the user who created the bucket. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), and then configure your CloudTrail inputs. The threshold value should be tuned to your environment. -action.escu.full_search_name = ESCU - Detect New Open S3 buckets - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Initial Access", "Exfiltration"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 13"], "nist": ["PR.DS", "PR.AC", "DE.CM"]} -action.escu.known_false_positives = While this search has no known false positives, it is possible that an AWS admin has legitimately created a public bucket for a specific purpose. That said, AWS strongly advises against granting full control to the "All Users" group. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect New Open S3 buckets -action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = An open/public S3 bucket, $bucketName$, was created by $user$. -action.notable.param.rule_title = Public S3 bucket $bucketName$ created by $user$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS S3 Bucket details via bucketName\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By ARN\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Investigate AWS activities via region name\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 70 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user,bucketName -alert.suppress.period = 86400s -cron_schedule = 5 * * * * -description = This search looks for CloudTrail events where a user has created an open/public S3 bucket. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail AllUsers eventName=PutBucketAcl | spath output=userIdentityArn path=userIdentity.arn | spath output=bucketName path=requestParameters.bucketName | spath output=aclControlList path=requestParameters.AccessControlPolicy.AccessControlList | spath input=aclControlList output=grantee path=Grant{} | mvexpand grantee | spath input=grantee | search Grantee.URI=*AllUsers | rename userIdentityArn as user| table _time, src,awsRegion Permission, Grantee.URI, bucketName, user - [ESCU - Get DNS traffic ratio] action.escu = 0 action.escu.enabled = 1 @@ -7874,7 +7917,7 @@ action.escu.full_search_name = ESCU - Get DNS traffic ratio action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Dynamic DNS", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Dynamic DNS", "Command and Control"] action.escu.fields_required = ["src_ip", "dest_ip"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 86400 @@ -7901,7 +7944,7 @@ action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["In action.escu.known_false_positives = None identified action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "SamSam Ransomware", "Monitor for Unauthorized Software"] +action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "Monitor for Unauthorized Software", "SamSam Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Prohibited Software On Endpoint action.notable = 1 @@ -7910,7 +7953,7 @@ action.notable.param.rule_description = Prohibited software $process_name$ has b action.notable.param.rule_title = Prohibited Software Detected On $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -7951,7 +7994,7 @@ action.escu.mappings = {"mitre_attack": ["Commonly Used Port", "Credential Acces action.escu.known_false_positives = It is likely that the outbound Server Message Block (SMB) traffic is legitimate, if the company's internal networks are not well-defined in the Assets and Identity Framework. Categorize the internal CIDR blocks as `internal` in the lookup file to avoid creating notable events for traffic destined to those CIDR blocks. Any other network connection that is going out to the Internet should be investigated and blocked. Best practices suggest preventing external communications of all SMB versions and related protocols at the network boundary. action.escu.search_type = detection action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Hidden Cobra Malware"] +action.escu.analytic_story = ["Hidden Cobra Malware", "DHS Report TA18-074A"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detect Outbound SMB Traffic action.notable = 1 @@ -7960,7 +8003,7 @@ action.notable.param.rule_description = Outbound SMB network traffic detected. action.notable.param.rule_title = Outbound SMB traffic from $src_ip$ to $dest_ip$ detected action.notable.param.security_domain = network action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Outbound Emails to Hidden Cobra Threat Actors\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src_ip @@ -7985,29 +8028,6 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb) by All_Traffic.src_ip All_Traffic.dest_ip | `drop_dm_object_name("All_Traffic")` | search ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | convert ctime(earliest) ctime(latest) -[ESCU - Count of Unique IPs Connecting to Ports] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-06-24 -action.escu.modification_date = 2017-09-13 -action.escu.channel = ESCU -action.escu.eli5 = For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections. -action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Count of Unique IPs Connecting to Ports -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch"] -description = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | sort - count - [ESCU - Detect Spike in Security Group Activity - Rule] action.escu = 0 action.escu.enabled = 1 @@ -8227,6 +8247,56 @@ schedule_window = auto is_visible = false search = sourcetype=WinEventLog:System EventCode=7036 | rex field=Message "The (?[\w\s-]*) service entered the (?\w*) state" | where action="running" | inputlookup append=t previously_seen_running_windows_services | multireport [| stats earliest(eval(coalesce(_time, firstTime))) as firstTime, latest(eval(coalesce(_time, lastTime))) as lastTime by serviceName | outputlookup previously_seen_running_windows_services | where fact=fiction] [| eventstats earliest(eval(coalesce(_time, firstTime))) as firstTime, latest(eval(coalesce(_time, lastTime))) as lastTime by serviceName | where firstTime >= relative_time(now(), "-60m@m") AND isnotnull(_time) | stats values(dest) as dest by _time, serviceName] | table _time, serviceName, dest +[ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2016-09-18 +action.escu.modification_date = 2018-12-03 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = This search looks for PowerShell processes that have a number of suspicious flags on the command-line. It is looking for flags are passing encoded commands on the command-line. The flags `-EncodedCommand` and `-enc` are two different possible flags that can be used to pass base64 encoded commands to PowerShell. The `*-Exec*` flag looks to see it the default execution policy of PowerShell is being overridden, while the `*-NonI*` flag tells the PowerShell process that this will be a noninteractive process, so the user doesn't know about the process. This search will return the host, the user the process ran under, the process and it's command-line arguments, the number of times it's seen this process, and the first and last times it saw this process. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "PowerShell", "Scripting"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 7", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +action.escu.known_false_positives = Legitimate process can have this combination of command-line options, but it's not common. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Malicious PowerShell"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Malicious PowerShell Process - Multiple Suspicious Command-Line Arguments +action.notable = 1 +action.notable.param.nes_fields = dest, user, process, process_name +action.notable.param.rule_description = The system $dest$ executed a PowerShell that had an encoded command on the command-line, attempted to bypass local execution policy, and prevented the display of an interactive prompt to the user. +action.notable.param.rule_title = PowerShell process with multiple suspicious command-line arguments detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = dest +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 60 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest, process_name +alert.suppress.period = 14400s +cron_schedule = 50 * * * * +description = This search looks for PowerShell processes started with a base64 encoded command-line passed to it, with parameters to modify the execution policy for the process, and those that prevent the display of an interactive prompt to the user. This combination of command-line options is suspicious because it overrides the default PowerShell execution policy, attempts to hide itself from the user, and passes an encoded script to be run on the command-line. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=powershell.exe by Processes.user Processes.process_name Processes.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| search (process=*-EncodedCommand* OR process=*-enc*) process=*-Exec* AND process=*-NonI* + [ESCU - Registry Keys Used For Persistence - Rule] action.escu = 0 action.escu.enabled = 1 @@ -8243,7 +8313,7 @@ action.escu.mappings = {"mitre_attack": ["Persistence", "Registry Run Keys / Sta action.escu.known_false_positives = There are many legitimate applications that must execute on system startup and will use these registry keys to accomplish that task. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "DHS Report TA18-074A", "Ransomware", "Emotet Malware (TA18-201A)", "Windows Persistence Techniques", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Ransomware", "Suspicious MSHTA Activity", "Suspicious Windows Registry Activities", "Windows Persistence Techniques", "Emotet Malware (TA18-201A)", "DHS Report TA18-074A", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Registry Keys Used For Persistence action.notable = 1 @@ -8277,45 +8347,45 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Registry.registry_key_name) as registry_key_name values(Registry.registry_path) as registry_path min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path=*currentversion\\run* OR Registry.registry_path=*currentVersion\\Windows\\Appinit_Dlls* OR Registry.registry_path=CurrentVersion\\Winlogon\\Shell* OR Registry.registry_path=*CurrentVersion\\Winlogon\\Userinit* OR Registry.registry_path=*CurrentVersion\\Winlogon\\VmApplet* OR Registry.registry_path=*currentversion\\policies\\explorer\\run* OR Registry.registry_path=*currentversion\\runservices* OR Registry.registry_path=*\\CurrentControlSet\\Control\\Lsa\\* OR Registry.registry_path="*Microsoft\\Windows NT\\CurrentVersion\\Image File Execution Options*" OR Registry.registry_path=HKLM\\SOFTWARE\\Microsoft\\Netsh\\*) by Registry.dest , Registry.status, Registry.user | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(Registry)` -[ESCU - SMB Traffic Spike - Rule] +[ESCU - Batch File Write to System32 - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-20 -action.escu.modification_date = 2017-09-10 +action.escu.creation_date = 2018-12-14 +action.escu.modification_date = 2018-12-14 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = Server Message Block (SMB) traffic, a protocol used for Windows file sharing-activity, is often leveraged by attackers. One example of SMB abuse was the WannaCry ransomware, which leveraged a vulnerability in the SMB protocol to propagate to other systems. Attackers have also used SMB for lateral movement with a target environment and to test credentials against target systems. While SMB is highly prevalent in Windows environments, a spike in SMB traffic may still be indicative of this type of malicious activity. This search looks for a traffic spike in SMB traffic from a particular system. If such a spike is detected, you may want to investigate the source and analyze the cause of the abnormal traffic. -action.escu.how_to_implement = This search requires you to be ingesting your network traffic logs and populating the `Network_Traffic` data model. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - SMB Traffic Spike - Rule -action.escu.mappings = {"mitre_attack": ["Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["DE.CM"]} -action.escu.known_false_positives = A file server may experience high-demand loads that could cause this analytic to trigger. +action.escu.confidence = high +action.escu.eli5 = This search looks at file modifications across your hosts, as well as for evidence of batch files being written to paths that include "system32." This activity is consistent with some SamSam attacks and is, in general, suspicious. +action.escu.how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Batch File Write to System32 - Rule +action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. action.escu.search_type = detection -action.escu.providing_technologies = ["Bro", "Splunk Stream"] -action.escu.analytic_story = ["DHS Report TA18-074A", "Ransomware", "Emotet Malware (TA18-201A)", "Hidden Cobra Malware"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +action.escu.analytic_story = ["SamSam Ransomware"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = SMB Traffic Spike +action.correlationsearch.label = Batch File Write to System32 action.notable = 1 -action.notable.param.nes_fields = src -action.notable.param.rule_description = There was a spike in SMB traffic from $src$. -action.notable.param.rule_title = SMB Traffic Spike from $src$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Outbound Emails to Hidden Cobra Threat Actors\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.nes_fields = dest, file_name +action.notable.param.rule_description = A batch file was written to the system directory on $dest$. +action.notable.param.rule_title = Batch file write to system32 detected on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = src +action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 +action.risk.param._risk_score = 80 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = src -alert.suppress.period = 28800s +alert.suppress.fields = dest,file_name +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = This search looks for spikes in the number of Server Message Block (SMB) traffic connections. -dispatch.earliest_time = -7d@d +description = The search looks for a batch file (.bat) written to the Windows system directory tree. +dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -8325,7 +8395,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src | `drop_dm_object_name("All_Traffic")` | eventstats max(_time) as maxtime | stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time upperBound AND num_data_samples >=50, 1, 0) | where isOutlier=1 | table src count +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat [ESCU - AWS Investigate User Activities By AccessKeyId] action.escu = 0 @@ -8449,45 +8519,45 @@ schedule_window = auto is_visible = false search = sourcetype=stream:http http_content_type=text* uri=/magento2/customer/account/loginPost* | rex field=form_data "login\[username\]=(?[^&|^$]+)" | rex field=form_data "login\[password\]=(?[^&|^$]+)" | stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password|where UniqueUsernames>5 -[ESCU - Windows hosts file modification - Rule] +[ESCU - Identify New User Accounts - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-06-07 -action.escu.modification_date = 2018-11-02 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2017-08-05 +action.escu.modification_date = 2017-09-12 +action.escu.asset_at_risk = Domain Server action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = The hosts file is present on both Windows and Linux endpoints. The purpose of the hosts file is to provide a mapping between hostnames and IP addresses, the same way DNS is used to provide such a mapping. However, the information in the hosts file takes precedence over information received via DNS and a DNS query will not be issued if the hostname of interest is found in the hosts file. As such, attackers have been observed adding entries to the host file to override any DNS resolution. For this reason, it is useful to monitor for changes to this file, which typically do not occur very often in legitimate cases. -action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records the file-system activity from your hosts to populate the Endpoint.Filesystem data model node. This is typically populated via endpoint detection-and-response products, such as Carbon Black, or by other endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report file-system reads and writes. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Windows hosts file modification - Rule -action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration"], "kill_chain_phases": ["Command and Control"], "cis20": ["CIS 3", "CIS 8", "CIS 12"], "nist": ["PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} -action.escu.known_false_positives = There may be legitimate reasons for system administrators to add entries to this file. +action.escu.confidence = medium +action.escu.eli5 = Adversaries will often seek to create new user accounts as a means of maintaining access to a target environment. Using this search, we identify accounts created in the last week by comparing the start date in the Identity_Management data model against the current time. +action.escu.how_to_implement = To successfully implement this search, you need to be populating the Enterprise Security Identity_Management data model in the assets and identity framework. +action.escu.data_models = ["Identity_Management"] +action.escu.full_search_name = ESCU - Identify New User Accounts - Rule +action.escu.mappings = {"mitre_attack": ["Valid Accounts"], "cis20": ["CIS 16"], "nist": ["PR.IP"]} +action.escu.known_false_positives = If the Identity_Management data model is not updated regularly, this search could give you false positive alerts. Please consider this and investigate appropriately. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Host Redirection"] +action.escu.providing_technologies = ["Active Directory"] +action.escu.analytic_story = ["Account Monitoring and Controls"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Windows hosts file modification +action.correlationsearch.label = Identify New User Accounts action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A file modification was noted for the hosts file on $dest$. -action.notable.param.rule_title = Modification of hosts file detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get Process responsible for the DNS traffic\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.nes_fields = user +action.notable.param.rule_description = Using the identities lookup and macro from Enterprise Security to identify (report) new users (6 month period) and temp users (3 months until account expiration) +action.notable.param.rule_title = Identify Temporary Users +action.notable.param.security_domain = access +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Logon Rights Modifications For Endpoint\n - ESCU - Get Logon Rights Modifications For User\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 -action.risk.param._risk_object = dest +action.risk.param._risk_object = user action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 +action.risk.param._risk_score = 40 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,user +alert.suppress.fields = identity alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = The search looks for modifications to the hosts file on all Windows endpoints across your environment. -dispatch.earliest_time = -70m@m +cron_schedule = 0 0 * * * +description = This detection search will help profile user accounts in your environment by identifying newly created accounts that have been added to your network in the past week. +dispatch.earliest_time = -24h@h dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -8497,56 +8567,30 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem by Filesystem.file_name Filesystem.file_path Filesystem.dest | `ctime(lastTime)` | `ctime(firstTime)` | search Filesystem.file_name=hosts AND Filesystem.file_path=*Windows\\System32\\* | `drop_dm_object_name(Filesystem)` +search = | from datamodel Identity_Management.All_Identities | eval empStatus=case((now()-startDate)<604800, "Accounts created in last week") | search empStatus="Accounts created in last week"| `ctime(endDate)` | `ctime(startDate)`| table identity empStatus endDate startDate -[ESCU - Attempt To Stop Security Service - Rule] +[ESCU - Identify Systems Using Remote Desktop] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-04-09 +action.escu.creation_date = 2017-04-18 action.escu.modification_date = 2017-09-15 -action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search looks for the processes **net.exe** and **sc.exe** with a parameter of `"stop"`. It then searches a list of security-related services included in a lookup file for matches on the command line. Results are subsequently returned in table format. The included lookup file can be modified to update the services to monitor. -action.escu.how_to_implement = You must be ingesting logs with both the process name and command line from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA. The search is shipped with a lookup file, `security_services.csv`, that can be edited to update the list of services to monitor. This lookup file can be edited directly where it lives in `$SPLUNK_HOME/etc/apps/DA-ESS-ContentUpdate/lookups`, or via the Splunk console. You should add the names of services an attacker might use on the command line and surround with asterisks (*****), so that they work properly when searching the command line. The file should be updated with the names of any services you would like to monitor for attempts to stop the service., -action.escu.full_search_name = ESCU - Attempt To Stop Security Service - Rule -action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Disabling Security Tools"], "kill_chain_phases": ["Installation", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 5", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -action.escu.known_false_positives = None identified. Attempts to disable security-related services should be identified and understood. -action.escu.search_type = detection +action.escu.eli5 = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. It does this by looking for the process in the Application_State data model. +action.escu.how_to_implement = To successfully implement this search you must be ingesting endpoint data that records process activity. +action.escu.data_models = ["Application_State"] +action.escu.full_search_name = ESCU - Identify Systems Using Remote Desktop +action.escu.known_false_positives = None at this time +action.escu.search_type = support action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Disabling Security Tools"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Attempt To Stop Security Service -action.notable = 1 -action.notable.param.nes_fields = dest, process, user -action.notable.param.rule_description = Attempt to stop a security-related service on $dest$ -action.notable.param.rule_title = Attempt to Stop Security Service On $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 50 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, user -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = This search looks for attempts to stop security-related services on the endpoint. -dispatch.earliest_time = -70m@m +action.escu.analytic_story = ["Lateral Movement"] +description = This search counts the numbers of times the remote desktop process, mstsc.exe, has run on each system. +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational (process=net.exe OR process=sc.exe) cmdline="* stop *" | lookup security_services_lookup service as cmdline OUTPUTNEW category, description | search category=security | table _time, dest, user, parent_process, cmdline, description +search = | tstats `summariesonly` count from datamodel=Application_State where All_Application_State.process="*mstsc.exe*" by All_Application_State.dest All_Application_State.process | `drop_dm_object_name("All_Application_State")` | sort - count [ESCU - WMI Permanent Event Subscription - Rule] action.escu = 0 @@ -8663,7 +8707,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "Accessibility Features"], action.escu.known_false_positives = None identified action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Unusual Processes", "Windows Privilege Escalation"] +action.escu.analytic_story = ["Windows Privilege Escalation", "Unusual Processes"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Uncommon Processes On Endpoint action.notable = 1 @@ -8672,7 +8716,7 @@ action.notable.param.rule_description = Prohibited software $process_name$ has b action.notable.param.rule_title = Prohibited Software Detected On $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -8697,44 +8741,143 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Application_State by All_Application_State.dest All_Application_State.user All_Application_State.process | `ctime(firstTime)`| `ctime(lastTime)` | `drop_dm_object_name("All_Application_State")` | `uncommon_processes` -[ESCU - Registry Keys for Creating SHIM Databases - Rule] +[ESCU - Remote Desktop Network Traffic - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-27 +action.escu.creation_date = 2016-09-13 action.escu.modification_date = 2017-09-15 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = In this search, we look for modifications to registry keys used for shim databases on Microsoft platforms via the object_category and object_path field in the Change_Analysis data model and give you the destination, command used to initiate the change, the user who conducted this activity, the resource affected(object), and the whole path of the object. An application compatibility shim is a small library that transparently intercepts an API (via hooking), changes the parameters passed, handles the operation itself, or redirects the operation elsewhere, such as additional code stored on a system. This capability can be also leveraged by attackers to create and store malicious files in a shim database as observed in CARBANAK backdoor. -action.escu.how_to_implement = To successfully implement this search, you must populate the Change_Analysis data model. This is typically populated via endpoint detection and response products, such as Carbon Black or other endpoint data sources such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry. -action.escu.data_models = ["Change_Analysis"] -action.escu.full_search_name = ESCU - Registry Keys for Creating SHIM Databases - Rule -action.escu.mappings = {"mitre_attack": ["Persistence", "Application Shimming"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.known_false_positives = There are many legitimate applications that leverage shim databases for compatibility purposes for legacy applications +action.escu.eli5 = This search finds systems that do not commonly communicate use remote desktop. It does this by filtering out all systems that have the "common_rdp_source" or "common_rdp_destination" category applied to that system. Categories are applied to systems using the Assets and Identity framework. +action.escu.how_to_implement = To successfully implement this search you need to identify systems that commonly originate remote desktop traffic and that commonly receive remote desktop traffic. You can use the included support search "Identify Systems Creating Remote Desktop Traffic" to identify systems that originate the traffic and the search "Identify Systems Receiving Remote Desktop Traffic" to identify systems that receive a lot of remote desktop traffic. After identifying these systems, you will need to add the "common_rdp_source" or "common_rdp_destination" category to that system depending on the usage, using the Enterprise Security Assets and Identities framework. This can be done by adding an entry in the assets.csv file located in SA-IdentityManagement/lookups. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Remote Desktop Network Traffic - Rule +action.escu.mappings = {"mitre_attack": ["Lateral Movement", "Remote Desktop Protocol", "Commonly Used Port"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 9", "CIS 16"], "nist": ["DE.AE", "PR.AC", "PR.IP"]} +action.escu.known_false_positives = Remote Desktop may be used legitimately by users on the network. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["Suspicious Windows Registry Activities", "Windows Persistence Techniques"] +action.escu.providing_technologies = ["Bro", "Splunk Stream"] +action.escu.analytic_story = ["Hidden Cobra Malware", "Lateral Movement", "SamSam Ransomware"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Registry Keys for Creating SHIM Databases +action.correlationsearch.label = Remote Desktop Network Traffic action.notable = 1 -action.notable.param.nes_fields = dest, user -action.notable.param.rule_description = A registry key that is used for persistence on Windows was modified on $dest$ by $user$ -action.notable.param.rule_title = Registry Key Associated With SHIM databases on $dest$ +action.notable.param.nes_fields = dest, src +action.notable.param.rule_description = Remote Desktop Traffic detected between $src$ and $dest$. These two systems typically do not communicate with RDP +action.notable.param.rule_title = Uncommon Remote Desktop Network Traffic between $src$ and $dest$ +action.notable.param.security_domain = network +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src +action.risk.param._risk_object_type = system +action.risk.param._risk_score = 50 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,src +alert.suppress.period = 28800s +cron_schedule = 0 * * * * +description = This search looks for network traffic on TCP/3389, the default port used by remote desktop. While remote desktop traffic is not uncommon on a network, it is usually associated with known hosts. This search allows for whitelisting both source and destination hosts to remove them from the output of the search so you can focus on the uncommon uses of remote desktop on your network. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Traffic where All_Traffic.dest_port=3389 AND All_Traffic.dest_category!=common_rdp_destination AND All_Traffic.src_category!=common_rdp_source by All_Traffic.src All_Traffic.dest All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | `ctime(firstTime)`| `ctime(lastTime)` + +[ESCU - Detect new API calls from user roles - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-04-01 +action.escu.modification_date = 2018-04-16 +action.escu.asset_at_risk = AWS Instance +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = The subsearch will execute first and return the user roles and names of the API calls completed within the last hour, where the type of user identity is `AssumedRole`. It then appends the historical data to those results in the lookup file. Next, it recalculates the `earliest` and `latest` fields for each user role, as well as the name of the API call, and returns only those roles and API calls that have first been seen in the past hour. This is combined with the main search to return the values of API calls, name of the user role, and the earliest and latest time of this activity. It is worth noting that the name of the role of a particular user is parsed as "userName" in the CloudTrail logs. +action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously seen API call per user roles in CloudTrail" support search once to create a history of previously seen user roles. +action.escu.full_search_name = ESCU - Detect new API calls from user roles - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.known_false_positives = It is possible that there are legitimate user roles making new or infrequently used API calls in your infrastructure, causing the search to trigger. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS User Monitoring"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect new API calls from user roles +action.notable = 1 +action.notable.param.nes_fields = user +action.notable.param.rule_description = A new API call made by $user$ has been detected. This API activity has either never been seen before or has not been seen within the last hour. +action.notable.param.rule_title = New API call by $user$ detected action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Investigate AWS User Activities by user field\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 10 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = user +alert.suppress.period = 86400s +cron_schedule = 30 * * * * +description = This search detects new API calls that have either never been seen before or that have not been seen in the previous hour, where the identity type is `AssumedRole`. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole [search sourcetype=aws:cloudtrail eventType=AwsApiCall errorCode=success userIdentity.type=AssumedRole | stats earliest(_time) as earliest latest(_time) as latest by userName eventName | inputlookup append=t previously_seen_api_calls_from_user_roles | stats min(earliest) as earliest, max(latest) as latest by userName eventName | outputlookup previously_seen_api_calls_from_user_roles| eval newApiCallfromUserRole=if(earliest>=relative_time(now(), "-70m@m"), 1, 0) | where newApiCallfromUserRole=1 | `ctime(earliest)` | `ctime(latest)` | table eventName userName] |rename userName as user| stats values(eventName) earliest(_time) as earliest latest(_time) as latest by user | `ctime(earliest)` | `ctime(latest)` + +[ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-10-07 +action.escu.modification_date = 2018-11-15 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = Obtaining access to the Command-Line Interface (CLI) is typically a primary attacker goal. Once an attacker has obtained the ability to execute code on a target system, they will often further manipulate the system via commands passed to the CLI. It is also unusual for many applications to spawn a command shell during normal operation, while it is often observed if an application has been compromised in some way. As such, it is often beneficial to look for cmd.exe being executed by processes that are often targeted for exploitation, or that would not spawn cmd.exe in any other circumstances. A lookup file is provided to easily modify the processes that are being watched for execution of cmd.exe. +action.escu.how_to_implement = You must be ingesting data that records process activity from your hosts and populates the Endpoint data model with the resultant dataset. This search includes a lookup file, `prohibited_apps_launching_cmd.csv`, that contains a list of processes that should not be spawning cmd.exe. You can modify this lookup to better suit your environment. +action.escu.data_models = ["Endpoint"] +action.escu.full_search_name = ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Exploitation"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.known_false_positives = There are circumstances where an application may legitimately execute and interact with the Windows command-line interface. Investigate and modify the lookup file, as appropriate. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Suspicious MSHTA Activity", "Suspicious Command-Line Executions"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Prohibited Applications Spawning cmd.exe +action.notable = 1 +action.notable.param.nes_fields = dest, process, parent_process +action.notable.param.rule_description = A prohibited application from prohibited_apps_launching_cmd.csv was leveraged to launch cmd.exe +action.notable.param.rule_title = Prohibited application($parent_process$) used to launch cmd.exe on $dest$ +action.notable.param.security_domain = endpoint +action.notable.param.severity = medium +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 +action.risk.param._risk_score = 80 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest,object_path +alert.suppress.fields = dest, parent_process alert.suppress.period = 86400s cron_schedule = 0 * * * * -description = This search looks for registry activity associated with application compatibility shims, which can be leveraged by attackers for various nefarious purposes. +description = This search looks for executions of cmd.exe spawned by a process that is often abused by attackers and that does not typically launch cmd.exe. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -8745,74 +8888,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Change_Analysis.All_Changes where All_Changes.object_category=registry AND (All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\Custom*" OR All_Changes.object_path="*CurrentVersion\\AppCompatFlags\\InstalledSDB*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `drop_dm_object_name("All_Changes")` - -[ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-02-01 -action.escu.modification_date = 2018-11-02 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search\ -\ -1. Retrieves the **AssumeRole** event\ -\ -1. Verifies that the log entry contains a value for the account ID of the requesting account\ -\ -1. Ensures that the requesting account ID does not match the account ID of the requested account\ -\ -1. Pulls in the previously seen requesting and requested account IDs\ -\ -1. Splits up and executes multiple search paths at the same.\ -\ -1. The first path determines the **firstTime** and **lastTime** entries for the cache file\ -\ -1. Outputs the data to the cache file.\ -\ -1. Creates a conditional statement that is always false (both because we don't want these values to exit the search pipeline and because we think we're clever).The second pipeline adds the **firstTime** and **lastTime** entries to search results. Next, it filters out any account pairs that haven't been seen for the first time within the last hour. The `isnotnull(_time)` will remove the entries from the cache file.\ -\ -The search finishes by gathering the data that it will display to the user. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. Run the `Previously Seen AWS Cross Account Activity` support search only once to create the baseline of previously seen cross account activity. Thanks to Pablo Vega at Recurly for suggesting improvements to the search. -action.escu.full_search_name = ESCU - AWS Cross Account Activity From Previously Unseen Account - Rule -action.escu.mappings = {"mitre_attack": ["Credential Access"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 16"], "nist": ["PR.AC", "PR.DS", "DE.AE"]} -action.escu.known_false_positives = Using multiple AWS accounts and roles is perfectly valid behavior. It's suspicious when an account requests privileges of an account it hasn't before. You should validate with the account owner that this is a legitimate request. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cross Account Activity"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cross Account Activity From Previously Unseen Account -action.notable = 1 -action.notable.param.nes_fields = requestingAccountId, requestedAccountId, src_user, dest_user -action.notable.param.rule_description = Access to $dest_user$ was requested for the first time by $src_user$ -action.notable.param.rule_title = AWS Account $dest_user$ access by $src_user$ -action.notable.param.security_domain = network -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Notable History\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - AWS Investigate User Activities By AccessKeyId\n - ESCU - AWS Investigate User Activities By Source User\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 20 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = requestingAccountId, requestedAccountId -alert.suppress.period = 14400s -cron_schedule = 5 * * * * -description = This search looks for AssumeRole events where an IAM role in a different account is requested for the first time. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=AssumeRole | spath output=requestingAccountId path=userIdentity.accountId | spath output=requestedAccountId path=resources{}.accountId | search requestingAccountId=* | where requestingAccountId != requestedAccountId | inputlookup append=t previously_seen_aws_cross_account_activity | multireport [| stats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | outputlookup previously_seen_aws_cross_account_activity | where fact=fiction] [| eventstats min(eval(coalesce(firstTime, strptime(_time,"%Y-%m-%d %H:%M:%S")))) as firstTime, max(eval(coalesce(strptime(_time,"%Y-%m-%d %H:%M:%S"), lastTime))) as lastTime by requestingAccountId, requestedAccountId | where firstTime >= relative_time(now(), "-70m@m") AND isnotnull(_time) | spath output=accessKeyId path=responseElements.credentials.accessKeyId | spath output=requestingARN path=resources{}.ARN | stats values(awsRegion) as awsRegion values(firstTime) as firstTime values(lastTime) as lastTime values(sharedEventID) as sharedEventID, values(requestingARN) as src_user, values(responseElements.assumedRoleUser.arn) as dest_user by _time, requestingAccountId, requestedAccountId, accessKeyId] | table _time, firstTime, lastTime, src_user, requestingAccountId, dest_user, requestedAccountId, awsRegion, accessKeyId, sharedEventID +search = | tstats `summariesonly` count values(Processes.user) as user values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe by Processes.parent_process Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] [ESCU - Remote Process Instantiation via WMI - Rule] action.escu = 0 @@ -8829,7 +8905,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instru action.escu.known_false_positives = The wmic.exe utility is a benign Windows application. It may be used legitimately by Administrators with these parameters for remote system administration, but it's relatively uncommon. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "Suspicious WMI Use"] +action.escu.analytic_story = ["Suspicious WMI Use", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Remote Process Instantiation via WMI action.notable = 1 @@ -8838,7 +8914,7 @@ action.notable.param.rule_description = This search looks for wmic.exe being lau action.notable.param.rule_title = Remote process instantiation via WMI on $dest$ action.notable.param.security_domain = endpoint action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Process Info\n - ESCU - Get Sysmon WMI Activity for Host\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -8863,56 +8939,6 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) process=*wmic* cmdline="*/node*" cmdline="*process*" cmdline="*call*" cmdline="*create*" | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` -[ESCU - Batch File Write to System32 - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-12-14 -action.escu.modification_date = 2018-12-14 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search looks at file modifications across your hosts, as well as for evidence of batch files being written to paths that include "system32." This activity is consistent with some SamSam attacks and is, in general, suspicious. -action.escu.how_to_implement = You must be ingesting data that records the file-system activity from your hosts to populate the Endpoint file-system data-model node. If you are using Sysmon, you will need a Splunk Universal Forwarder on each endpoint from which you want to collect data. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Batch File Write to System32 - Rule -action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.known_false_positives = It is possible for this search to generate a notable event for a batch file write to a path that includes the string "system32", but is not the actual Windows system directory. As such, you should confirm the path of the batch file identified by the search. In addition, a false positive may be generated by an administrator copying a legitimate batch file in this directory tree. You should confirm that the activity is legitimate and modify the search to add exclusions, as necessary. -action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -action.escu.analytic_story = ["SamSam Ransomware"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Batch File Write to System32 -action.notable = 1 -action.notable.param.nes_fields = dest, file_name -action.notable.param.rule_description = A batch file was written to the system directory on $dest$. -action.notable.param.rule_title = Batch file write to system32 detected on $dest$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = high -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Backup Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get Update Logs For Endpoint\n - ESCU - Get User Information from Identity Table\n - ESCU - Get Vulnerability Logs For Endpoint\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Parent Process Info\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Investigate Successful Remote Desktop Authentications\n - ESCU - Investigate Web Activity From Host\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 80 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest,file_name -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = The search looks for a batch file (.bat) written to the Windows system directory tree. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Filesystem.dest) as dest values(Filesystem.file_name) as file_name values(Filesystem.user) as user from datamodel=Endpoint.Filesystem by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)`| rex field=file_name "(?\.[^\.]+)$" | search file_path=*system32* AND file_extension=.bat - [ESCU - Windows Updates Install Successes] action.escu = 0 action.escu.enabled = 1 @@ -8998,7 +9024,7 @@ action.escu.full_search_name = ESCU - Get Parent Process Info action.escu.known_false_positives = None at this time action.escu.search_type = investigative action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Credential Dumping", "Suspicious Windows Registry Activities", "DHS Report TA18-074A", "Suspicious Command-Line Executions", "Windows Defense Evasion Tactics", "Windows File Extension and Association Abuse", "Collection and Staging", "Ransomware", "Windows Service Abuse", "Orangeworm Attack Group", "Emotet Malware (TA18-201A)", "Disabling Security Tools", "Hidden Cobra Malware", "Windows Privilege Escalation", "SamSam Ransomware", "Windows Persistence Techniques", "Netsh Abuse", "Suspicious MSHTA Activity", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Ransomware", "Suspicious MSHTA Activity", "Hidden Cobra Malware", "Suspicious Windows Registry Activities", "Windows File Extension and Association Abuse", "Windows Persistence Techniques", "Credential Dumping", "Collection and Staging", "Emotet Malware (TA18-201A)", "Windows Service Abuse", "Suspicious Command-Line Executions", "Windows Privilege Escalation", "DHS Report TA18-074A", "Orangeworm Attack Group", "Disabling Security Tools", "Windows Defense Evasion Tactics", "SamSam Ransomware", "Netsh Abuse", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] action.escu.fields_required = ["process", "dest"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 86400 @@ -9047,7 +9073,7 @@ action.escu.mappings = {"mitre_attack": ["Command and Control", "Exfiltration", action.escu.known_false_positives = It's possible that normal DNS traffic will exhibit this behavior. If an alert is generated, please investigate and validate as appropriate. The threshold can also be modified to better suit your environment. action.escu.search_type = detection action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Data Protection", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Data Protection", "Suspicious DNS Traffic", "Command and Control"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detection of DNS Tunnels action.notable = 1 @@ -9056,7 +9082,7 @@ action.notable.param.rule_description = Potential DNS tunnel detected from $src$ action.notable.param.rule_title = DNS tunnel detected on $src$ action.notable.param.security_domain = network action.notable.param.severity = low -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process responsible for the DNS traffic\n"} +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - AWS Network Interface details via resourceId\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get DNS Server History for a host\n - ESCU - Get DNS traffic ratio\n - ESCU - Get Process Info\n - ESCU - Get Process Information For Port Activity\n - ESCU - Get Process responsible for the DNS traffic\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -9118,7 +9144,7 @@ action.escu.mappings = {"mitre_attack": ["Execution"], "kill_chain_phases": ["Ac action.escu.known_false_positives = Some legitimate applications start with long command-lines. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Suspicious Command-Line Executions", "Ransomware", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Ransomware", "Suspicious Command-Line Executions", "Unusual Processes", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Unusually Long Command Line action.notable = 1 @@ -9152,127 +9178,101 @@ schedule_window = auto is_visible = false search = (sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational OR tag=process) | stats count min(_time) as firstTime max(_time) as lastTime by dest, user, process, cmdline | `ctime(firstTime)`| `ctime(lastTime)` | eval cmdlen=len(cmdline) | eventstats stdev(cmdlen) as stdev, avg(cmdlen) as avg by dest | stats max(cmdlen) as maxlen, values(stdev) as stdevperhost, values(avg) as avgperhost by dest, process,cmdline| eval threshold = 10 | where maxlen > ((threshold*stdevperhost) + avgperhost) -[ESCU - Baseline of blocked outbound traffic from AWS] +[ESCU - Email Attachments With Lots Of Spaces - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-04-26 -action.escu.modification_date = 2018-05-07 +action.escu.creation_date = 2017-04-21 +action.escu.modification_date = 2017-09-19 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.eli5 = Use this search to create a baseline of blocked outbound network connections by each source IP in your AWS environment. This search returns all log events that correspond to a blocked outbound network connection, extracts the source IP from where the outbound connection was initiated, and collects the events in one-hour groupings. Next, it calculates the number of outbound connections blocked per hour. For each source IP, it calculates the average and standard deviation of this count on a per-hour basis. It also includes the number of data points each source IP had. This table is then stored in a lookup file. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow logs.`. -action.escu.full_search_name = ESCU - Baseline of blocked outbound traffic from AWS +action.escu.confidence = high +action.escu.eli5 = This search looks at any emails with file attachment names that contain many spaces relative to the length of the file name. Specifically, it checks if spaces make up more than 10% of the number of characters in the file name. This percentage can be tuned for each environment. The search will then output the message ID of the email, the count, the recipient address and the recipient user, first and last time this event was seen and the space ratio of the file attachment name. +action.escu.how_to_implement = You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment. +action.escu.data_models = ["Email"] +action.escu.full_search_name = ESCU - Email Attachments With Lots Of Spaces - Rule +action.escu.mappings = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 7"], "nist": ["PR.IP"]} +action.escu.known_false_positives = None at this time +action.escu.search_type = detection +action.escu.providing_technologies = ["Microsoft Exchange"] +action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "Suspicious Emails"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Email Attachments With Lots Of Spaces +action.notable = 1 +action.notable.param.nes_fields = src_user, file_name +action.notable.param.rule_description = The sender $src_user$ has sent an email with a suspicious amount of spaces in the file name: $file_name$ +action.notable.param.rule_title = Suspicious Email Attachment from $src_user$ +action.notable.param.security_domain = network +action.notable.param.severity = high +action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get Authentication Logs For Endpoint\n - ESCU - Get Notable History\n - ESCU - Get Risk Modifiers For Endpoint\n - ESCU - Get Risk Modifiers For User\n - ESCU - Get User Information from Identity Table\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get Email Info\n - ESCU - Get Emails From Specific Sender\n - ESCU - Investigate Web Activity From Host\n"} +action.notable.param.recommended_actions = escu_contextualize, escu_investigate +action.risk = 1 +action.risk.param._risk_object = src_user +action.risk.param._risk_object_type = user +action.risk.param._risk_score = 60 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = src_user +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = Attackers often use spaces as a means to obfuscate an attachment's file extension. This search looks for messages with email attachments that have many spaces within the filename. +dispatch.earliest_time = -70m@m +dispatch.latest_time = -10m@m +disabled=true +enableSched = 1 +counttype = number of events +relation = greater than +quantity = 0 +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats `summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Email")` | eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name) | search space_ratio >= 0.1 | rex field=recipient_address "(?.*)@" + +[ESCU - Count of Unique IPs Connecting to Ports] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-06-24 +action.escu.modification_date = 2017-09-13 +action.escu.channel = ESCU +action.escu.eli5 = For each port being accessed on the network, this search gives the total number of connections observed, and the number of unique IP addresses making those connections. +action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Count of Unique IPs Connecting to Ports action.escu.known_false_positives = None at this time action.escu.search_type = support -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS Traffic", "Command and Control", "AWS Network ACL Activity"] -description = This search establishes, on a per-hour basis, the average and the standard deviation of the number of outbound connections blocked in your VPC flow logs by each source IP address (IP address of your EC2 instances). Also recorded is the number of data points for each source IP. This table outputs to a lookup file to allow the detection search to operate quickly. +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch"] +description = The search counts the number of times a connection was observed to each destination port, and the number of unique source IPs connecting to them. dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudwatchlogs:vpcflow action=blocked (src_ip=10.0.0.0/8 OR src_ip=172.16.0.0/12 OR src_ip=192.168.0.0/16) ( dest_ip!=10.0.0.0/8 AND dest_ip!=172.16.0.0/12 AND dest_ip!=192.168.0.0/16) | bucket _time span=1h | stats count as numberOfBlockedConnections by _time, src_ip | stats count(numberOfBlockedConnections) as numDataPoints, latest(numberOfBlockedConnections) as latestCount, avg(numberOfBlockedConnections) as avgBlockedConnections, stdev(numberOfBlockedConnections) as stdevBlockedConnections by src_ip | table src_ip, latestCount, numDataPoints, avgBlockedConnections, stdevBlockedConnections | outputlookup baseline_blocked_outbound_connections | stats count +search = | tstats `summariesonly` count dc(All_Traffic.src) as numberOfUniqueHosts from datamodel=Network_Traffic by All_Traffic.dest_port | `drop_dm_object_name("All_Traffic")` | sort - count -[ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] +[ESCU - Identify Systems Receiving Remote Desktop Traffic] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-16 -action.escu.modification_date = 2018-03-16 -action.escu.asset_at_risk = AWS Instance +action.escu.creation_date = 2017-04-24 +action.escu.modification_date = 2017-09-15 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = The subsearch returns all events with event names that start with "Run" or "Create," and then does a `GeoIP` lookup on the IP address that initiated the action within the last hour. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for each country, region, city, and IP address and outputs this data to the lookup file to update the local cache. It then calculates the `firstTime` and `lastTime` for each city. It returns only those events from IP addresses that have first been seen in the past hour. This is combined with the main search to return the time, user, IP address, city, event name, and error code from the action. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen AWS Provisioning Activity Sources" support search once to create a history of previously seen locations that have provisioned AWS resources. -action.escu.full_search_name = ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.known_false_positives = This is a strictly behavioral search, so we define "false positive" slightly differently. Every time this fires, it will accurately reflect the first occurrence in the time period you're searching within, plus what is stored in the cache feature. But while there are really no "false positives" in a traditional sense, there is definitely lots of noise.\ -\ - This search will fire any time a new IP address is seen in the **GeoIP** database for any kind of provisioning activity. If you typically do all provisioning from tools inside of your country, there should be few false positives. If you are located in countries where the free version of **MaxMind GeoIP** that ships by default with Splunk has weak resolution (particularly small countries in less economically powerful regions), this may be much less valuable to you. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Suspicious Provisioning Activities"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen IP Address -action.notable = 1 -action.notable.param.nes_fields = src_ip -action.notable.param.rule_description = Your AWS infrastructure was provisioned from an IP, $src_ip$, which has never before been seen provisioning your infrastructure. -action.notable.param.rule_title = AWS Provision Activity From $src_ip$ -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From City\n - ESCU - Get All AWS Activity From Country\n - ESCU - Get All AWS Activity From IP Address\n - ESCU - Get All AWS Activity From Region\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = src_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = src_ip -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for AWS provisioning activities from previously unseen IP addresses. Provisioning activities are defined broadly as any event that begins with "Run" or "Create." -dispatch.earliest_time = -70m@m +action.escu.eli5 = This search counts the numbers of times the system has received a connection to TCP/ 3389, the default port used for RDP traffic. +action.escu.how_to_implement = To successfully implement this search you must ingest network traffic and populate the Network_Traffic data model. If a system receives a lot of remote desktop traffic, you can apply the category common_rdp_destination to it. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Identify Systems Receiving Remote Desktop Traffic +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Lateral Movement"] +description = This search counts the numbers of times the system has created remote desktop traffic +dispatch.earliest_time = -30d@d dispatch.latest_time = -10m@m disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* [search sourcetype=aws:cloudtrail (eventName=Run* OR eventName=Create*) | iplocation sourceIPAddress | search Country=* | stats earliest(_time) as firstTime, latest(_time) as lastTime by sourceIPAddress, City, Region, Country | inputlookup append=t previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress, City, Region, Country | outputlookup previously_seen_provisioning_activity_src.csv | stats min(firstTime) as firstTime max(lastTime) as lastTime by sourceIPAddress | eval newIP=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newIP=1 | table sourceIPAddress] | spath output=user userIdentity.arn | rename sourceIPAddress as src_ip | table _time, user, src_ip, eventName, errorCode - -[ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-03-12 -action.escu.modification_date = 2018-03-12 -action.escu.asset_at_risk = AWS Instance -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = The subsearch returns the instance types of all successful EC2 instance launches within the last hour and then appends the historical data in the lookup file to those results. It then recalculates the earliest seen time field for each instance type and returns only those instance types that has first been seen in the past hour. This is combined with the main search to return the time, user, and instance id of those systems. -action.escu.how_to_implement = You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your CloudTrail inputs. This search works best when you run the "Previously Seen EC2 Instance Types" support search once to create a history of previously seen instance types. -action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.known_false_positives = It is possible that an admin will create a new system using a new instance type never used before. Verify with the creator that they intended to create the system with the new instance type. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Started With Previously Unseen Instance Type -action.notable = 1 -action.notable.param.nes_fields = instanceType -action.notable.param.rule_description = The EC2 instance type $instanceType$ was used for the first time to create $dest$. -action.notable.param.rule_title = New EC2 Instance Type $instanceType$ detected -action.notable.param.security_domain = endpoint -action.notable.param.severity = medium -action.notable.param.next_steps = {"version": 1, "data": "Recommended following steps:\n\n1. [[action|escu_contextualize]]: Based on ESCU context gathering recommendations:\n - ESCU - Get EC2 Launch Details\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n"} -action.notable.param.recommended_actions = escu_contextualize, escu_investigate -action.risk = 1 -action.risk.param._risk_object = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for EC2 instances being created with previously unseen instance types. -dispatch.earliest_time = -70m@m -dispatch.latest_time = -10m@m -disabled=true -enableSched = 1 -counttype = number of events -relation = greater than -quantity = 0 -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = sourcetype=aws:cloudtrail eventName=RunInstances [search sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | fillnull value="m1.small" requestParameters.instanceType | stats earliest(_time) as earliest latest(_time) as latest by requestParameters.instanceType | rename requestParameters.instanceType as instanceType | inputlookup append=t previously_seen_ec2_instance_types.csv | stats min(earliest) as earliest max(latest) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | eval newType=if(earliest >= relative_time(now(), "-70m@m"), 1, 0) | convert ctime(earliest) ctime(latest) | where newType=1 | rename instanceType as requestParameters.instanceType | table requestParameters.instanceType] | spath output=user userIdentity.arn | rename requestParameters.instanceType as instanceType, responseElements.instancesSet.items{}.instanceId as dest | table _time, user, dest, instanceType +search = | tstats `summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - count [ESCU - Get Risk Modifiers For Endpoint] action.escu = 0 @@ -9287,7 +9287,7 @@ action.escu.full_search_name = ESCU - Get Risk Modifiers For Endpoint action.escu.known_false_positives = None at this time action.escu.search_type = contextual action.escu.providing_technologies = ["Splunk Enterprise Security"] -action.escu.analytic_story = ["Apache Struts Vulnerability", "Credential Dumping", "Suspicious Windows Registry Activities", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "Host Redirection", "Collection and Staging", "Ransomware", "Windows Service Abuse", "Command and Control", "Splunk Enterprise Vulnerability CVE-2018-11409", "ColdRoot MacOS RAT", "Orangeworm Attack Group", "SQL Injection", "Emotet Malware (TA18-201A)", "Disabling Security Tools", "Use of Cleartext Protocols", "Suspicious WMI Use", "Prohibited Traffic Allowed or Protocol Mismatch", "Hidden Cobra Malware", "Unusual Processes", "Data Protection", "Brand Monitoring", "Asset Tracking", "Account Monitoring and Controls", "Windows Privilege Escalation", "ColdRoot MacOS RAT", "SamSam Ransomware", "Monitor for Unauthorized Software", "Windows Persistence Techniques", "Malicious PowerShell", "DNS Amplification Attacks", "Spectre And Meltdown Vulnerabilities", "Dynamic DNS", "Lateral Movement", "Suspicious DNS Traffic", "Splunk Enterprise Vulnerability", "Netsh Abuse", "Windows Log Manipulation", "Monitor Backup Solution", "Router & Infrastructure Security", "Suspicious Emails", "Suspicious MSHTA Activity", "JBoss Vulnerability", "Monitor for Updates", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] +action.escu.analytic_story = ["Suspicious WMI Use", "ColdRoot MacOS RAT", "JBoss Vulnerability", "Data Protection", "Ransomware", "Suspicious MSHTA Activity", "Hidden Cobra Malware", "Windows Log Manipulation", "Suspicious Windows Registry Activities", "Splunk Enterprise Vulnerability", "Windows File Extension and Association Abuse", "Suspicious DNS Traffic", "Windows Persistence Techniques", "Dynamic DNS", "Apache Struts Vulnerability", "Credential Dumping", "Monitor for Updates", "Spectre And Meltdown Vulnerabilities", "Collection and Staging", "Emotet Malware (TA18-201A)", "Monitor for Unauthorized Software", "DNS Amplification Attacks", "Suspicious Emails", "SQL Injection", "Monitor Backup Solution", "Windows Service Abuse", "Windows Privilege Escalation", "Asset Tracking", "DHS Report TA18-074A", "Account Monitoring and Controls", "Malicious PowerShell", "Orangeworm Attack Group", "Router & Infrastructure Security", "Brand Monitoring", "Use of Cleartext Protocols", "Disabling Security Tools", "Command and Control", "Splunk Enterprise Vulnerability CVE-2018-11409", "Prohibited Traffic Allowed or Protocol Mismatch", "Unusual Processes", "Host Redirection", "Lateral Movement", "SamSam Ransomware", "Netsh Abuse", "ColdRoot MacOS RAT", "Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0