diff --git a/src/default/analytic_stories.conf b/src/default/analytic_stories.conf index 2e5657c627..bd448b1439 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"] @@ -241,7 +241,7 @@ data_models = ["Application_State", "Authentication", "Network_Resolution", "Net description = Detect and investigate tactics, techniques, and procedures leveraged by attackers to establish and operate command and control channels. Implants installed by attackers on compromised endpoints use these channels to receive instructions and send data back to the malicious operators. id = 943773c6-c4de-4f38-89a8-0b92f98804d8 version = 1.0 -mappings = {"mitre_attack": ["Command and Control", "Exfiltration Over Command and Control Channel", "Standard Non-Application Layer Protocol", "Commonly Used Port", "Exfiltration Over Alternative Protocol", "Exfiltration", "Standard Application Layer Protocol", "Defense Evasion"], "cis20": ["CIS 8", "CIS 9", "CIS 11", "CIS 12", "CIS 13", "CIS 3", "CIS 1"], "kill_chain_phases": ["Command and Control", "Actions on Objectives", "Delivery"], "nist": ["ID.AM", "PR.DS", "PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} +mappings = {"mitre_attack": ["Command and Control", "Exfiltration Over Command and Control Channel", "Standard Non-Application Layer Protocol", "Commonly Used Port", "Exfiltration Over Alternative Protocol", "Exfiltration", "Standard Application Layer Protocol", "Defense Evasion"], "cis20": ["CIS 8", "CIS 9", "CIS 11", "CIS 12", "CIS 13", "CIS 3", "CIS 1"], "kill_chain_phases": ["Command and Control", "Delivery", "Actions on Objectives"], "nist": ["ID.AM", "PR.DS", "PR.IP", "PR.PT", "PR.AC", "DE.AE", "DE.CM"]} modification_date = 2018-07-24 reference = ["https://attack.mitre.org/wiki/Command_and_Control", "https://searchsecurity.techtarget.com/feature/Command-and-control-servers-The-puppet-masters-that-govern-malware"] providing_technologies = ["AWS", "Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] @@ -799,7 +799,7 @@ data_models = ["Application_State", "Authentication", "Endpoint", "Network_Traff description = Leverage searches that allow you to detect and investigate unusual activities that might relate to the SamSam ransomware, including looking for file writes associated with SamSam, RDP brute force attacks, the presence of files with SamSam ransomware extensions, suspicious psexec use, and more. id = c4b89506-fbcf-4cb7-bfd6-527e54789604 version = 1.0 -mappings = {"mitre_attack": ["Exploitation of Vulnerability", "System Information Discovery", "Commonly Used Port", "Command-Line Interface", "Credential Access", "Lateral Movement", "Defense Evasion", "Execution", "Remote Desktop Protocol", "Discovery"], "cis20": ["CIS 3", "CIS 18", "CIS 8", "CIS 9", "CIS 10", "CIS 12", "CIS 2", "CIS 4", "CIS 16"], "kill_chain_phases": ["Delivery", "Actions on Objectives", "Reconnaissance", "Installation", "Command and Control"], "nist": ["ID.AM", "PR.DS", "ID.RA", "PR.IP", "PR.PT", "PR.AC", "DE.AE", "PR.MA", "DE.CM"]} +mappings = {"mitre_attack": ["Exploitation of Vulnerability", "Execution", "Commonly Used Port", "Command-Line Interface", "Credential Access", "Lateral Movement", "Defense Evasion", "System Information Discovery", "Remote Desktop Protocol", "Discovery"], "cis20": ["CIS 3", "CIS 18", "CIS 8", "CIS 9", "CIS 10", "CIS 12", "CIS 2", "CIS 4", "CIS 16"], "kill_chain_phases": ["Delivery", "Actions on Objectives", "Reconnaissance", "Installation", "Command and Control"], "nist": ["ID.AM", "PR.DS", "ID.RA", "PR.IP", "PR.PT", "PR.AC", "DE.AE", "PR.MA", "DE.CM"]} modification_date = 2018-12-14 reference = ["https://www.crowdstrike.com/blog/an-in-depth-analysis-of-samsam-ransomware-and-boss-spider/", "https://www.sophos.com/en-us/medialibrary/PDFs/technical-papers/SamSam-ransomware-chooses-Its-targets-carefully-wpna.pdf", "https://www.sophos.com/en-us/medialibrary/PDFs/technical-papers/SamSam-The-Almost-Six-Million-Dollar-Ransomware.pdf?cmp=26061"] providing_technologies = ["Apache", "Bluecoat", "Bro", "Carbon Black Response", "CrowdStrike Falcon", "Linux", "Microsoft Windows", "Nessus", "Netbackup", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream", "Sysmon", "Tanium", "Ziften", "macOS"] @@ -1147,7 +1147,7 @@ data_models = ["Email"] description = Monitor your environment for activity consistent with common attack techniques bad actors use when attempting to compromise web servers or other web-related assets. id = 31337aaa-bc22-4752-b599-ef112dq1dq7a version = 1.0 -mappings = {"mitre_attack": ["Valid Accounts", "Create Account"], "cis20": ["CIS 6", "CIS 16"], "kill_chain_phases": ["Actions on Objectives"], "nist": ["DE.DP", "DE.AE", "DE.CM"]} +mappings = {"mitre_attack": ["Valid Accounts", "Create Account"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 6", "CIS 16"], "nist": ["DE.CM", "DE.AE", "DE.DP"]} modification_date = 2018-10-08 reference = ["https://www.fbi.gov/scams-and-safety/common-fraud-schemes/internet-fraud", "https://www.fbi.gov/news/stories/2017-internet-crime-report-released-050718", "https://www.otalliance.org/news-events/press-releases/online-trust-alliance-reports-doubling-cyber-incidents-2017-0"] providing_technologies = ["Bro", "Microsoft Exchange", "Palo Alto Firewall", "Splunk Enterprise Security", "Splunk Stream"] diff --git a/src/default/analyticstories.conf b/src/default/analyticstories.conf index 38549f5073..7b1935b6e0 100644 --- a/src/default/analyticstories.conf +++ b/src/default/analyticstories.conf @@ -1031,15 +1031,14 @@ known_false_positives = It is possible that these logs may be legitimately clear providing_technologies = ["Microsoft Windows"] -[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 - Create or delete hidden shares using net.exe - Rule] @@ -1063,14 +1062,37 @@ known_false_positives = None at the moment providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[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 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 - 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 - Web Fraud - Password Sharing Across Accounts - Rule] +type = detection +asset_type = account +confidence = medium +explanation = A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5 +how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. +annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} +known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. +providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Bro"] [savedsearch://ESCU - TOR Traffic - Rule] @@ -1113,22 +1135,15 @@ known_false_positives = None at this time providing_technologies = ["Netbackup"] -[savedsearch://ESCU - Get Logon Rights Modifications For Endpoint] -type = investigative -explanation = none -how_to_implement = To successfully implement this search you must be ingesting your Windows event logs -known_false_positives = None at this time -providing_technologies = ["Microsoft Windows"] -earliest_time_offset = 86400 -latest_time_offset = 86400 - - -[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. -how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. -known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro"] +[savedsearch://ESCU - Detect API activity from users without MFA - Rule] +type = detection +asset_type = AWS Instance +confidence = medium +explanation = In this search, we query CloudTrail logs and specifically look for events where the multi factor authentication context of the user's session is false which basically means, that the user does not have MFA enabled on AWS. We then filter out all the known AWS service accounts since service accounts typically do not have MFA enabled. The search then creates a table of the first and last time a user without MFA was detected, the values and count of the API calls made, the type of user identity, ARN and the name of 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. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them. +annotations = {"mitre_attack": ["Execution"], "cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} +known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. +providing_technologies = ["AWS"] [savedsearch://ESCU - Baseline of Security Group Activity by ARN] @@ -1139,33 +1154,15 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] +[savedsearch://ESCU - Unsuccessful Netbackup backups - Rule] type = detection -asset_type = AWS Instance -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"] +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"]} +known_false_positives = None identified +providing_technologies = ["Netbackup"] [savedsearch://ESCU - Detect processes used for System Network Configuration Discovery - Rule] @@ -1179,15 +1176,15 @@ known_false_positives = It is uncommon for normal users to execute a series of c providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - WMI Permanent Event Subscription - Rule] +[savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule] type = detection -asset_type = Endpoint +asset_type = AWS Instance confidence = medium -explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Windows event ID 5861. -how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -annotations = {"mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} -known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. -providing_technologies = ["Microsoft Windows"] +explanation = In this search, we query CloudTrail logs to look for events that indicate that an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The `eval` and `if` functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with "Instance Started in a New Region". However, this region will be added to the list of `previously_seen_aws_regions.csv`. Please maintain `previously_seen_aws_regions.csv` +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 Regions" support search only once to create of baseline of previously seen regions. +annotations = {"mitre_attack": ["Defense Evasion"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 12"], "nist": ["DE.DP", "DE.AE"]} +known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. +providing_technologies = ["AWS"] [savedsearch://ESCU - Get Outbound Emails to Hidden Cobra Threat Actors] @@ -1287,14 +1284,14 @@ 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 - Detect S3 access from a new IP - Rule] +[savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - 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 +asset_type = AWS Instance +confidence = medium +explanation = The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro `ec2ModificationAPIs`. The search then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have 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 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2ModificationAPIs`. +annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. providing_technologies = ["AWS"] @@ -1335,8 +1332,8 @@ providing_technologies = ["Bro", "Splunk Stream"] type = detection asset_type = Endpoint confidence = medium -explanation = Using a lookup discover_dns_records generated by support search "Discover DNS records" we check previous network traffic and make sure the responses have not changed. -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 the discover_dns_record lookup table be populated by the included support search "Discover DNS record". \ +explanation = Using a lookup `discover_dns_records` generated by support search "Discover DNS records" we check previous network traffic and make sure the responses have not changed. +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 the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ \ **Splunk>Phantom Playbook Integration**\ \ @@ -1361,12 +1358,15 @@ known_false_positives = As is common with many fraud-related searches, we are us providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Bro"] -[savedsearch://ESCU - Discover DNS records] -type = support -explanation = Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv` -how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation -known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro"] +[savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] +type = detection +asset_type = Web Server +confidence = medium +explanation = This search returns the number of times a URL associated with this type of JexBoss probe is observed. +how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. +annotations = {"mitre_attack": ["Discovery", "System Information Discovery"], "kill_chain_phases": ["Reconnaissance"]} +known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. +providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] [savedsearch://ESCU - SMB Traffic Spike - Rule] @@ -1380,6 +1380,19 @@ known_false_positives = A file server may experience high-demand loads that coul providing_technologies = ["Bro", "Splunk Stream"] +[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 - Samsam Test File Write - Rule] type = detection asset_type = Endpoint @@ -1391,6 +1404,14 @@ known_false_positives = No false positives have been identified. providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +[savedsearch://ESCU - Previously Seen AWS Cross Account Activity] +type = support +explanation = In this support search, we look for **AssumeRole** events where the requesting account is different from the requested account. The first and last times these events are seen are written to 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. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. +known_false_positives = None at this time +providing_technologies = ["AWS"] + + [savedsearch://ESCU - Investigate AWS activities via region name] type = investigative explanation = none @@ -1401,16 +1422,6 @@ 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 @@ -1433,17 +1444,6 @@ 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 @@ -1508,15 +1508,15 @@ known_false_positives = Some of these processes may be used legitimately on web providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Large Volume of DNS ANY Queries - Rule] +[savedsearch://ESCU - Schtasks used for forcing a reboot - Rule] type = detection -asset_type = DNS Servers -confidence = high -explanation = This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set. -how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. -annotations = {"kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11", "CIS 12"], "nist": ["PR.PT", "DE.AE", "PR.IP"]} -known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. -providing_technologies = ["Splunk Stream", "Bro"] +asset_type = Endpoint +confidence = medium +explanation = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled that would cause a forced reboot on the 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 establish persistence. This tactic is leveraged by the Bad Rabbit Ransomware. +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", "Execution", "Scheduled Task"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} +known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] [savedsearch://ESCU - Previously Seen EC2 Launches By User] @@ -1527,15 +1527,15 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Detect Long DNS TXT Record Response - Rule] +[savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] type = detection -asset_type = Endpoint +asset_type = Windows 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"] +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 - Suspicious Reg.exe Process - Rule] @@ -1560,12 +1560,15 @@ known_false_positives = Administrators may attempt to change the default executi providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Previously Seen AWS Cross Account Activity] -type = support -explanation = In this support search, we look for **AssumeRole** events where the requesting account is different from the requested account. The first and last times these events are seen are written to 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. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. -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 - Suspicious writes to windows Recycle Bin - Rule] @@ -1579,25 +1582,22 @@ known_false_positives = Because the Recycle Bin is a hidden folder in modern ver providing_technologies = ["Sysmon"] -[savedsearch://ESCU - Detect API activity from users without MFA - Rule] -type = detection -asset_type = AWS Instance -confidence = medium -explanation = In this search, we query CloudTrail logs and specifically look for events where the multi factor authentication context of the user's session is false which basically means, that the user does not have MFA enabled on AWS. We then filter out all the known AWS service accounts since service accounts typically do not have MFA enabled. The search then creates a table of the first and last time a user without MFA was detected, the values and count of the API calls made, the type of user identity, ARN and the name of 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. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them. -annotations = {"mitre_attack": ["Execution"], "cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} -known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. -providing_technologies = ["AWS"] - - -[savedsearch://ESCU - All backup logs for host] +[savedsearch://ESCU - Get Logon Rights Modifications For Endpoint] type = investigative explanation = none -how_to_implement = The successfully implement this search you must first send your backup logs to Splunk. +how_to_implement = To successfully implement this search you must be ingesting your Windows event logs known_false_positives = None at this time -providing_technologies = ["Netbackup"] -earliest_time_offset = 1209600 -latest_time_offset = 0 +providing_technologies = ["Microsoft Windows"] +earliest_time_offset = 86400 +latest_time_offset = 86400 + + +[savedsearch://ESCU - Previously Seen Running Windows Services] +type = support +explanation = In this support search, we look for Windows system-event code that indicates a status change of a Windows service. It extracts both the name of the service and the action taken by the service from the logs. It keeps only services that have entered the running state. Finally, it finds the first time the service has been seen running across the enterprise and writes that file to a lookup table. +how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. +known_false_positives = None at this time +providing_technologies = ["Microsoft Windows"] [savedsearch://ESCU - Clients Connecting to Multiple DNS Servers - Rule] @@ -1611,6 +1611,16 @@ 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 @@ -1622,46 +1632,22 @@ 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. +[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 = ["Sysmon"] -earliest_time_offset = 7200 -latest_time_offset = 7200 +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 - Detect USB device insertion - 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"] - - -[savedsearch://ESCU - Get Backup Logs For Endpoint] +[savedsearch://ESCU - Get User Information from Identity Table] type = contextual explanation = none -how_to_implement = You must be ingesting your backup logs. +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 = ["Netbackup"] -earliest_time_offset = 604800 -latest_time_offset = 0 +providing_technologies = ["Splunk Enterprise Security"] +earliest_time_offset = 864000 +latest_time_offset = 86400 [savedsearch://ESCU - Get DNS Server History for a host] @@ -1685,15 +1671,12 @@ known_false_positives = There are no known false positives. providing_technologies = ["OSquery"] -[savedsearch://ESCU - Unsuccessful Netbackup backups - 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"]} -known_false_positives = None identified -providing_technologies = ["Netbackup"] +[savedsearch://ESCU - Previously Seen EC2 Instance Types] +type = support +explanation = In this support search, we create a table of the earliest and latest time that a specific EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. 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 - Remote Registry Key modifications - Rule] @@ -1707,15 +1690,15 @@ known_false_positives = This technique may be legitimately used by administrator providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] -[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 - Get Notable Info] @@ -1772,12 +1755,22 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Previously Seen AWS Regions] +[savedsearch://ESCU - Identify Systems Creating Remote Desktop Traffic] 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. +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. +how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. known_false_positives = None at this time -providing_technologies = ["AWS"] +providing_technologies = ["Splunk Stream", "Bro"] + + +[savedsearch://ESCU - Get Process responsible for the DNS traffic] +type = investigative +explanation = none +how_to_implement = You must be ingesting endpoint data that associates processes with network events. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon. +known_false_positives = None at this time +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +earliest_time_offset = 3600 +latest_time_offset = 86400 [savedsearch://ESCU - WMI Temporary Event Subscription - Rule] @@ -1802,17 +1795,6 @@ known_false_positives = This registry key may be modified via administrators to providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - EC2 Instance Started In Previously Unseen Region - Rule] -type = detection -asset_type = AWS Instance -confidence = medium -explanation = In this search, we query CloudTrail logs to look for events that indicate that an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The `eval` and `if` functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with "Instance Started in a New Region". However, this region will be added to the list of `previously_seen_aws_regions.csv`. Please maintain `previously_seen_aws_regions.csv` -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 Regions" support search only once to create of baseline of previously seen regions. -annotations = {"mitre_attack": ["Defense Evasion"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 12"], "nist": ["DE.DP", "DE.AE"]} -known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -providing_technologies = ["AWS"] - - [savedsearch://ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule] type = detection asset_type = AWS Instance @@ -1848,15 +1830,33 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[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"] +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 - Previously seen S3 bucket access by remote IP] @@ -1867,17 +1867,6 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[savedsearch://ESCU - Execution of File with Multiple Extensions - Rule] -type = detection -asset_type = Endpoint -confidence = high -explanation = This search uses the "Application State" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the "process" field that match what you're looking for. -how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. -annotations = {"mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 8"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} -known_false_positives = None identified. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] - - [savedsearch://ESCU - Common Ransomware Notes - Rule] type = detection asset_type = Endpoint @@ -1963,12 +1952,15 @@ known_false_positives = It is unlikely that a normal user may create and place t providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Tanium", "Ziften"] -[savedsearch://ESCU - Previously Seen EC2 Instance Types] -type = support -explanation = In this support search, we create a table of the earliest and latest time that a specific EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. 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 - First time seen command line argument - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = The subsearch returns all events where `cmd.exe` was used with a `/c` parameter in the command-line arguments to execute other commands/programs. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past four hours. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument. +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 Technology Add-on (TA). Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. +annotations = {"mitre_attack": ["Execution", "Scripting", "Persistence", "Command-Line Interface"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +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. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] [savedsearch://ESCU - Suspicious Email Attachment Extensions - Rule] @@ -2011,22 +2003,24 @@ earliest_time_offset = 14400 latest_time_offset = 0 -[savedsearch://ESCU - Previously Seen Running Windows Services] -type = support -explanation = In this support search, we look for Windows system-event code that indicates a status change of a Windows service. It extracts both the name of the service and the action taken by the service from the logs. It keeps only services that have entered the running state. Finally, it finds the first time the service has been seen running across the enterprise and writes that file to a lookup table. -how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. -known_false_positives = None at this time -providing_technologies = ["Microsoft Windows"] - - -[savedsearch://ESCU - Get User Information from Identity Table] -type = contextual +[savedsearch://ESCU - All backup logs for host] +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 = The successfully implement this search you must first send your backup logs to Splunk. known_false_positives = None at this time -providing_technologies = ["Splunk Enterprise Security"] -earliest_time_offset = 864000 -latest_time_offset = 86400 +providing_technologies = ["Netbackup"] +earliest_time_offset = 1209600 +latest_time_offset = 0 + + +[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 - Child Processes of Spoolsv.exe - Rule] @@ -2040,21 +2034,14 @@ known_false_positives = Some legitimate printer-related processes may show up as providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Get Process responsible for the DNS traffic] -type = investigative -explanation = none -how_to_implement = You must be ingesting endpoint data that associates processes with network events. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon. -known_false_positives = None at this time -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -earliest_time_offset = 3600 -latest_time_offset = 86400 - - -[savedsearch://ESCU - Systems Ready for Spectre-Meltdown Windows Patch] -type = support -explanation = This search looks to see if a registry key was created at `HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat`. It will tell you when it was created and, if possible, what process created it. -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. -known_false_positives = None at this time +[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"] @@ -2099,48 +2086,25 @@ 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 = -providing_technologies = ["AWS"] +[savedsearch://ESCU - Get Vulnerability Logs For Endpoint] +type = contextual +explanation = none +how_to_implement = You need to be ingesting the logs from your vulnerability scanner. +known_false_positives = None at this time +providing_technologies = ["Nessus"] +earliest_time_offset = 604800 +latest_time_offset = 0 -[savedsearch://ESCU - Web Fraud - Password Sharing Across Accounts - Rule] +[savedsearch://ESCU - Detect USB device insertion - Rule] type = detection -asset_type = account -confidence = medium -explanation = A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5 -how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. -annotations = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} -known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. -providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Bro"] +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"] [savedsearch://ESCU - Shim Database Installation With Suspicious Parameters - Rule] @@ -2154,15 +2118,15 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[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 - Detect Excessive User Account Lockouts - Rule] @@ -2176,13 +2140,13 @@ known_false_positives = It is possible that a legitimate user is experiencing an providing_technologies = ["Microsoft Windows"] -[savedsearch://ESCU - Get Vulnerability Logs For Endpoint] -type = contextual +[savedsearch://ESCU - Get All AWS Activity From City] +type = investigative explanation = none -how_to_implement = You need to be ingesting the logs from your vulnerability scanner. +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 = ["Nessus"] -earliest_time_offset = 604800 +providing_technologies = ["AWS"] +earliest_time_offset = 14400 latest_time_offset = 0 @@ -2208,6 +2172,35 @@ known_false_positives = The false-positive rate will vary based on how you set t providing_technologies = ["Bro", "Splunk Stream"] +[savedsearch://ESCU - Detect Spike in blocked Outbound Traffic from your AWS - Rule] +type = detection +asset_type = AWS Instance +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"] + + [savedsearch://ESCU - Identify New User Accounts - Rule] type = detection asset_type = Domain Server @@ -2219,6 +2212,17 @@ known_false_positives = If the Identity_Management data model is not updated reg 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 +providing_technologies = ["AWS"] + + [savedsearch://ESCU - Detect Large Outbound ICMP Packets - Rule] type = detection asset_type = Endpoint @@ -2230,17 +2234,6 @@ known_false_positives = ICMP packets are used in a variety of ways to help troub providing_technologies = ["Bro", "Splunk Stream", "Palo Alto Firewall"] -[savedsearch://ESCU - First time seen command line argument - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = The subsearch returns all events where `cmd.exe` was used with a `/c` parameter in the command-line arguments to execute other commands/programs. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past four hours. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument. -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 Technology Add-on (TA). Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. -annotations = {"mitre_attack": ["Execution", "Scripting", "Persistence", "Command-Line Interface"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -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. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] - - [savedsearch://ESCU - Get Logon Rights Modifications For User] type = investigative explanation = none @@ -2251,15 +2244,12 @@ earliest_time_offset = 86400 latest_time_offset = 86400 -[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 - 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 - Scheduled Task Name Used by Dragonfly Threat Actors - Rule] @@ -2388,25 +2378,12 @@ 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. +[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"] -earliest_time_offset = 14400 -latest_time_offset = 0 - - -[savedsearch://ESCU - EC2 Instance Modified With Previously Unseen User - Rule] -type = detection -asset_type = AWS Instance -confidence = medium -explanation = The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro `ec2ModificationAPIs`. The search then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have 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 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2ModificationAPIs`. -annotations = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. -providing_technologies = ["AWS"] [savedsearch://ESCU - AWS S3 Bucket details via bucketName] @@ -2454,15 +2431,12 @@ known_false_positives = Legitimate router connections may appear as new connecti providing_technologies = ["Active Directory", "Palo Alto Firewall"] -[savedsearch://ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] -type = detection -asset_type = Web Server -confidence = medium -explanation = This search returns the number of times a URL associated with this type of JexBoss probe is observed. -how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. -annotations = {"mitre_attack": ["Discovery", "System Information Discovery"], "kill_chain_phases": ["Reconnaissance"]} -known_false_positives = It's possible for legitimate HTTP requests to be made to URLs containing the suspicious paths. -providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Apache", "Bro"] +[savedsearch://ESCU - Discover DNS records] +type = support +explanation = Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv` +how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation +known_false_positives = None at this time +providing_technologies = ["Splunk Stream", "Bro"] [savedsearch://ESCU - Investigate Successful Remote Desktop Authentications] @@ -2475,15 +2449,15 @@ 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] @@ -2497,25 +2471,6 @@ 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 - Detect new user AWS Console Login - 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"] - - [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. @@ -2546,15 +2501,15 @@ known_false_positives = Although unlikely, administrators may use wmi to execute providing_technologies = ["Carbon Black Response", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] +[savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - 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 = 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 - Get All AWS Activity From Country] @@ -2618,14 +2573,6 @@ known_false_positives = None at this time providing_technologies = ["AWS"] -[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 @@ -2686,25 +2633,63 @@ earliest_time_offset = 86400 latest_time_offset = 0 -[savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule] -type = detection -asset_type = Endpoint -confidence = high -explanation = This search looks at all the URLs an endpoint is connecting to and then checks the URL against a list of faux domains that could be indicative of brand abuse. -how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. -annotations = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 7"], "nist": ["PR.IP"]} -known_false_positives = None at this time -providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] - - -[savedsearch://ESCU - EC2 Instance Started With Previously Unseen Instance Type - Rule] +[savedsearch://ESCU - Detect Spike in AWS API Activity - 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. +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 PsExec With accepteula Flag - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = In this search, we are looking for the PsExec process with `accepteula` on the command line. +how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, command-line arguments, and parent process from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon Technology Add-on (TA). +annotations = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine +providing_technologies = ["Sysmon"] + + +[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 - 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"] @@ -2719,17 +2704,6 @@ known_false_positives = Using sc.exe to manipulate Windows services is uncommon. providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[savedsearch://ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] -type = detection -asset_type = Endpoint -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"] - - [savedsearch://ESCU - Detect Spike in S3 Bucket deletion - Rule] type = detection asset_type = S3 Bucket @@ -2794,15 +2768,15 @@ earliest_time_offset = 14400 latest_time_offset = 0 -[savedsearch://ESCU - Schtasks used for forcing a reboot - Rule] +[savedsearch://ESCU - Large Volume of DNS ANY Queries - 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 that would cause a forced reboot on the 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 establish persistence. This tactic is leveraged by the Bad Rabbit Ransomware. -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", "Execution", "Scheduled Task"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} -known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. -providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +asset_type = DNS Servers +confidence = high +explanation = This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set. +how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. +annotations = {"kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11", "CIS 12"], "nist": ["PR.PT", "DE.AE", "PR.IP"]} +known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. +providing_technologies = ["Splunk Stream", "Bro"] [savedsearch://ESCU - Count of assets by category] @@ -2839,6 +2813,28 @@ 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 @@ -2860,15 +2856,14 @@ earliest_time_offset = 3600 latest_time_offset = 0 -[savedsearch://ESCU - Detect PsExec With accepteula Flag - Rule] -type = detection -asset_type = Endpoint -confidence = medium -explanation = In this search, we are looking for the PsExec process with `accepteula` on the command line. -how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, command-line arguments, and parent process from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon Technology Add-on (TA). -annotations = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine -providing_technologies = ["Sysmon"] +[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 - Reg.exe Manipulating Windows Services Registry Keys - Rule] @@ -2938,35 +2933,17 @@ 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] +[savedsearch://ESCU - Execution of File with Multiple Extensions - 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. +confidence = high +explanation = This search uses the "Application State" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the "process" field that match what you're looking for. +how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. +annotations = {"mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 8"], "nist": ["DE.CM", "PR.PT", "PR.IP"]} +known_false_positives = None identified. 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 @@ -2986,15 +2963,12 @@ known_false_positives = None at this time providing_technologies = ["Microsoft Windows"] -[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 - 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 - Single Letter Process On Endpoint - Rule] @@ -3030,14 +3004,15 @@ known_false_positives = There are no known false positives. providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Apache"] -[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 - 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 - Monitor DNS For Brand Abuse - Rule] @@ -3102,12 +3077,23 @@ earliest_time_offset = 14400 latest_time_offset = 0 -[savedsearch://ESCU - Baseline of API Calls per User ARN] +[savedsearch://ESCU - Attempted Credential Dump From Registry Via Reg.exe - Rule] +type = detection +asset_type = Endpoint +confidence = High +explanation = This search looks for the process reg.exe with the "save" parameter, which specifies a binary export from the registry. In addition, it looks for the keys that contain the hashed credentials, which attackers may retrieve and use for brute-force attacks in order to harvest legitimate credentials. +how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +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 = None identified. +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] + + +[savedsearch://ESCU - Systems Ready for Spectre-Meltdown Windows Patch] 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. +explanation = This search looks to see if a registry key was created at `HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat`. It will tell you when it was created and, if possible, what process created it. +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. known_false_positives = None at this time -providing_technologies = ["AWS"] +providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] [savedsearch://ESCU - Get EC2 Launch Details] @@ -3120,44 +3106,33 @@ earliest_time_offset = 7200 latest_time_offset = 0 -[savedsearch://ESCU - AWS Cross Account Activity From Previously Unseen Account - 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. -providing_technologies = ["AWS"] - - -[savedsearch://ESCU - Email Attachments With Lots Of Spaces - Rule] +[savedsearch://ESCU - Attempt To Add Certificate To Untrusted Store - 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"]} +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 - 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] @@ -3221,14 +3196,6 @@ 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 @@ -3360,6 +3327,16 @@ known_false_positives = There may be legitimate reasons for system administrator providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +[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 - Attempt To Stop Security Service - Rule] type = detection asset_type = Endpoint @@ -3371,6 +3348,17 @@ known_false_positives = None identified. Attempts to disable security-related se providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +[savedsearch://ESCU - WMI Permanent Event Subscription - Rule] +type = detection +asset_type = Endpoint +confidence = medium +explanation = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Windows event ID 5861. +how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. +annotations = {"mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 5"], "nist": ["PR.PT", "PR.AT", "PR.AC", "PR.IP"]} +known_false_positives = Although unlikely, administrators may use event subscriptions for legitimate purposes. +providing_technologies = ["Microsoft Windows"] + + [savedsearch://ESCU - Previously seen API call per user roles in CloudTrail] type = support explanation = In this support search, we are looking for successful API calls made by user roles within your AWS infrastructure. The intent is to create an initial baseline cache of names of the API calls per security role for the previous 30 days--including the earliest and latest times seen in our dataset--grouped by the value of user role and the name of the API call. It is also worth noting that the role of a particular user is parsed as "userName" in the CloudTrail logs. @@ -3390,17 +3378,6 @@ known_false_positives = None identified providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -[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 - Registry Keys for Creating SHIM Databases - Rule] type = detection asset_type = Endpoint @@ -3412,6 +3389,17 @@ known_false_positives = There are many legitimate applications that leverage shi providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon"] +[savedsearch://ESCU - Monitor Web Traffic For Brand Abuse - Rule] +type = detection +asset_type = Endpoint +confidence = high +explanation = This search looks at all the URLs an endpoint is connecting to and then checks the URL against a list of faux domains that could be indicative of brand abuse. +how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. +annotations = {"mitre_attack": [], "kill_chain_phases": ["Delivery"], "cis20": ["CIS 7"], "nist": ["PR.IP"]} +known_false_positives = None at this time +providing_technologies = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] + + [savedsearch://ESCU - Remote Process Instantiation via WMI - Rule] type = detection asset_type = Endpoint @@ -3442,6 +3430,17 @@ known_false_positives = It's possible that a user has legitimately deleted a net 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 - EC2 Instance Started With Previously Unseen AMI - Rule] type = detection asset_type = AWS Instance @@ -3472,14 +3471,14 @@ known_false_positives = It's possible that normal DNS traffic will exhibit this providing_technologies = ["Splunk Stream", "Bro"] -[savedsearch://ESCU - Attempted Credential Dump From Registry Via Reg.exe - Rule] +[savedsearch://ESCU - Malicious PowerShell Process - Encoded Command - Rule] type = detection asset_type = Endpoint -confidence = High -explanation = This search looks for the process reg.exe with the "save" parameter, which specifies a binary export from the registry. In addition, it looks for the keys that contain the hashed credentials, which attackers may retrieve and use for brute-force attacks in order to harvest legitimate credentials. -how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. -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 = None identified. +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"] @@ -3493,35 +3492,36 @@ 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] +[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 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. +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 = 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. +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] type = contextual explanation = none diff --git a/src/default/savedsearches.conf b/src/default/savedsearches.conf index 362074a3b2..f8f38a1ebe 100644 --- a/src/default/savedsearches.conf +++ b/src/default/savedsearches.conf @@ -13,7 +13,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Indicator Removal o action.escu.known_false_positives = It is possible that these logs may be legitimately cleared by Administrators. action.escu.search_type = detection action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation"] +action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Windows Event Log Cleared action.notable = 1 @@ -22,7 +22,7 @@ action.notable.param.rule_description = The Event Logging System has been cleare action.notable.param.rule_title = Windows Event Log Cleared 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 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 Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -47,54 +47,29 @@ 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 - Detect Activity Related to Pass the Hash Attacks - Rule] +[ESCU - Get Process Information For Port Activity] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-09-13 -action.escu.modification_date = 2017-09-18 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2017-06-25 +action.escu.modification_date = 2017-09-10 action.escu.channel = ESCU -action.escu.confidence = low -action.escu.eli5 = 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. -action.escu.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. -action.escu.full_search_name = ESCU - Detect Activity Related to Pass the Hash Attacks - Rule -action.escu.mappings = {"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"]} -action.escu.known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. -action.escu.search_type = detection -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Lateral Movement"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Activity Related to Pass the Hash Attacks -action.notable = 1 -action.notable.param.nes_fields = -action.notable.param.rule_description = This search looks for Authentication log events from the Windows Security Audit logs to detect potential attempts for Passing the Hash -action.notable.param.rule_title = Detect Activity Related to Pass the Hash -action.notable.param.security_domain = access -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 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 = 10 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = ComputerName -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. -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 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 = ["Command and Control", "SamSam Ransomware", "Ransomware", "Use of Cleartext Protocols", "Prohibited Traffic Allowed or Protocol Mismatch"] +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 -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=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 +search = | from datamodel Application_State.Ports | search dest_port={dest_port} dest={src} | table dest dest_port process process_name [ESCU - Create or delete hidden shares using net.exe - Rule] action.escu = 0 @@ -160,7 +135,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Persistence"], "kil 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 = ["Windows Persistence Techniques", "Suspicious Windows Registry Activities", "Windows Defense Evasion Tactics"] +action.escu.analytic_story = ["Windows Persistence Techniques", "Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Reg.exe used to hide files/directories via registry keys action.notable = 1 @@ -169,7 +144,7 @@ action.notable.param.rule_description = Regedit.exe is used by attackers to hide 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.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.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -194,29 +169,153 @@ 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] +[ESCU - Detect Activity Related to Pass the Hash Attacks - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-06-25 -action.escu.modification_date = 2017-09-10 +action.escu.creation_date = 2016-09-13 +action.escu.modification_date = 2017-09-18 +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 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 = ["SamSam Ransomware", "Prohibited Traffic Allowed or Protocol Mismatch", "Use of Cleartext Protocols", "Ransomware", "Command and Control"] -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. +action.escu.confidence = low +action.escu.eli5 = 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. +action.escu.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. +action.escu.full_search_name = ESCU - Detect Activity Related to Pass the Hash Attacks - Rule +action.escu.mappings = {"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"]} +action.escu.known_false_positives = Legitimate logon activity by authorized NTLM systems may be detected by this search. Please investigate as appropriate. +action.escu.search_type = detection +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Lateral Movement"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect Activity Related to Pass the Hash Attacks +action.notable = 1 +action.notable.param.nes_fields = +action.notable.param.rule_description = This search looks for Authentication log events from the Windows Security Audit logs to detect potential attempts for Passing the Hash +action.notable.param.rule_title = Detect Activity Related to Pass the Hash +action.notable.param.security_domain = access +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 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 = 10 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = ComputerName +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for specific authentication events from the Windows Security Event logs to detect potential attempts at using the Pass-the-Hash technique. +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 Application_State.Ports | search dest_port={dest_port} dest={src} | table dest dest_port process process_name +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 - Detect Long DNS TXT Record Response - Rule] +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.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 = ["Suspicious DNS Traffic", "Command and Control"] +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 - 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 = 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 +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" + +[ESCU - Web Fraud - Password Sharing Across Accounts - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-07-12 +action.escu.modification_date = 2018-10-08 +action.escu.asset_at_risk = account +action.escu.channel = webfraud +action.escu.confidence = medium +action.escu.eli5 = A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5 +action.escu.how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. +action.escu.full_search_name = ESCU - Web Fraud - Password Sharing Across Accounts - Rule +action.escu.mappings = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} +action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. +action.escu.search_type = detection +action.escu.providing_technologies = ["Splunk Stream", "Palo Alto Firewall", "Bro"] +action.escu.analytic_story = ["Web Fraud Detection"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Web Fraud - Password Sharing Across Accounts +action.notable = 1 +action.notable.param.nes_fields = user +action.notable.param.rule_description = This search is used to identify user accounts, $user$, that share common passwords +action.notable.param.rule_title = Web Fraud Detection: Password Sharing Across Accounts +action.notable.param.security_domain = threat +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 - Get Emails From Specific Sender\n - ESCU - Get Web Session Information via session_id\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 = other +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 = 3600s +cron_schedule = 0 * * * * +description = This search is used to identify user accounts that share a common password. +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=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 - TOR Traffic - Rule] action.escu = 0 @@ -234,7 +333,7 @@ action.escu.mappings = {"mitre_attack": ["Command and Control", "Commonly Used P action.escu.known_false_positives = None at this time action.escu.search_type = detection action.escu.providing_technologies = ["Palo Alto Firewall", "Bro", "Splunk Stream"] -action.escu.analytic_story = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "Ransomware", "Prohibited Traffic Allowed or Protocol Mismatch"] action.correlationsearch.enabled = 1 action.correlationsearch.label = TOR Traffic action.notable = 1 @@ -243,7 +342,7 @@ action.notable.param.rule_description = Network traffic accessing TOR detected f action.notable.param.rule_title = TOR 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 - 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.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 @@ -284,7 +383,7 @@ 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 @@ -293,7 +392,7 @@ 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 @@ -364,51 +463,54 @@ 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 Logon Rights Modifications For Endpoint] +[ESCU - Detect API activity from users without MFA - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-16 -action.escu.modification_date = 2017-09-12 +action.escu.creation_date = 2018-05-17 +action.escu.modification_date = 2018-05-17 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU -action.escu.eli5 = none -action.escu.how_to_implement = To successfully implement this search you must be ingesting your Windows event logs -action.escu.full_search_name = ESCU - Get Logon Rights Modifications For Endpoint -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Account Monitoring and Controls"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 86400 -action.escu.latest_time_offset = 86400 -description = This search allows you to retrieve any modifications to logon rights associated with a specific host. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | search sourcetype=WinEventLog:Security (EventCode=4718 OR EventCode=4717) dest={dest} | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature - -[ESCU - Identify Systems Creating Remote Desktop Traffic] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-04-24 -action.escu.modification_date = 2017-09-15 -action.escu.channel = ESCU -action.escu.eli5 = 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. -action.escu.how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. -action.escu.data_models = ["Network_Traffic"] -action.escu.full_search_name = ESCU - Identify Systems Creating 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 generated remote desktop traffic. -dispatch.earliest_time = -30d@d +action.escu.confidence = medium +action.escu.eli5 = In this search, we query CloudTrail logs and specifically look for events where the multi factor authentication context of the user's session is false which basically means, that the user does not have MFA enabled on AWS. We then filter out all the known AWS service accounts since service accounts typically do not have MFA enabled. The search then creates a table of the first and last time a user without MFA was detected, the values and count of the API calls made, the type of user identity, ARN and the name of 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. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them. +action.escu.full_search_name = ESCU - Detect API activity from users without MFA - Rule +action.escu.mappings = {"mitre_attack": ["Execution"], "cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} +action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. +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 API activity from users without MFA +action.notable = 1 +action.notable.param.nes_fields = user +action.notable.param.rule_description = API Activity detected from $user$ without MFA enabled. +action.notable.param.rule_title = API Activity detected from $user$ without MFA enabled +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.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 = 84600s +cron_schedule = 0 8 * * * +description = This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. +dispatch.earliest_time = -1d@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 from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.src | `drop_dm_object_name("All_Traffic")` | sort - count +search = sourcetype=aws:cloudtrail userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) by userIdentity.arn userIdentity.type user | `ctime(firstTime)` | `ctime(lastTime)` [ESCU - Baseline of Security Group Activity by ARN] action.escu = 0 @@ -432,62 +534,44 @@ 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 - Detect Spike in blocked Outbound Traffic from your AWS - Rule] +[ESCU - Unsuccessful Netbackup backups - 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.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 = 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.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 = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS Traffic", "Command and Control", "AWS Network ACL Activity"] +action.escu.providing_technologies = ["Netbackup"] +action.escu.analytic_story = ["Monitor Backup Solution"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in blocked Outbound Traffic from your AWS +action.correlationsearch.label = Unsuccessful Netbackup backups 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.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 = 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 = src_ip +action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 +action.risk.param._risk_score = 10 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 +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 dispatch.latest_time = -10m@m disabled=true enableSched = 1 @@ -497,7 +581,7 @@ 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 +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 @@ -549,43 +633,43 @@ 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 by Processes.dest Processes.process_name Processes.user _time | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(Processes)` | search `system_network_configuration_discovery_tools` | transaction dest maxpause=5m |where eventcount>=5 | table firstTime lastTime dest user process_name process parent_process eventcount -[ESCU - WMI Permanent Event Subscription - Rule] +[ESCU - EC2 Instance Started In Previously Unseen Region - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-10-23 -action.escu.modification_date = 2018-10-23 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2018-02-01 +action.escu.modification_date = 2018-02-23 +action.escu.asset_at_risk = AWS Instance action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Windows event ID 5861. -action.escu.how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. -action.escu.full_search_name = ESCU - WMI Permanent Event Subscription - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "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 = Although unlikely, administrators may use event subscriptions for legitimate purposes. +action.escu.eli5 = In this search, we query CloudTrail logs to look for events that indicate that an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The `eval` and `if` functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with "Instance Started in a New Region". However, this region will be added to the list of `previously_seen_aws_regions.csv`. Please maintain `previously_seen_aws_regions.csv` +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 Regions" support search only once to create of baseline of previously seen regions. +action.escu.full_search_name = ESCU - EC2 Instance Started In Previously Unseen Region - Rule +action.escu.mappings = {"mitre_attack": ["Defense Evasion"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 12"], "nist": ["DE.DP", "DE.AE"]} +action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. action.escu.search_type = detection -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Suspicious WMI Use"] +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = WMI Permanent Event Subscription +action.correlationsearch.label = EC2 Instance Started In Previously Unseen Region action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = This search looks for the creation of a permanent WMI event subscription via Windows event logs. -action.notable.param.rule_title = WMI Event Subscription Detected on $dest$ -action.notable.param.security_domain = endpoint +action.notable.param.nes_fields = awsRegion +action.notable.param.rule_description = An AWS instance is started in a new, previously unseen, region +action.notable.param.rule_title = AWS instance is started in a new region +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 - 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 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 = dest -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 +action.risk.param._risk_object = awsRegion +action.risk.param._risk_object_type = other +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 = 28800s +alert.suppress.fields = awsRegion +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = This search looks for the creation of WMI permanent event subscriptions. +description = This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -596,7 +680,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = sourcetype="wineventlog:microsoft-windows-wmi-activity/operational" EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `ctime(firstTime)`| `ctime(lastTime)` | rename ComputerName as dest +search = sourcetype=aws:cloudtrail earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion| inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(), "-1d@d"), "Instance Started in a New Region","Previously Seen Region") | convert ctime(earliest) ctime(latest) | where regionStatus="Instance Started in a New Region" [ESCU - Get Outbound Emails to Hidden Cobra Threat Actors] action.escu = 0 @@ -808,7 +892,7 @@ action.escu.mappings = {"mitre_attack": ["Command and Control", "Commonly Used P 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 = ["Prohibited Traffic Allowed or Protocol Mismatch", "Command and Control"] +action.escu.analytic_story = ["Command and Control", "Prohibited Traffic Allowed or Protocol Mismatch"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Protocol or Port Mismatch action.notable = 1 @@ -817,7 +901,7 @@ action.notable.param.rule_description = This search looks for network traffic on action.notable.param.rule_title = Protocol / Port Mismatch 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 - 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.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 @@ -914,43 +998,43 @@ 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 - Detect S3 access from a new IP - Rule] +[ESCU - EC2 Instance Modified With Previously Unseen User - 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.creation_date = 2018-04-09 +action.escu.modification_date = 2018-04-09 +action.escu.asset_at_risk = AWS Instance 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.confidence = medium +action.escu.eli5 = The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro `ec2ModificationAPIs`. The search then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have 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 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2ModificationAPIs`. +action.escu.full_search_name = ESCU - EC2 Instance Modified With Previously Unseen User - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. action.escu.search_type = detection action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS S3 Activities"] +action.escu.analytic_story = ["Unusual AWS EC2 Modifications"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect S3 access from a new IP +action.correlationsearch.label = EC2 Instance Modified With Previously Unseen User 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.nes_fields = user, dest +action.notable.param.rule_description = The EC2 instance $dest$ was modified by $user$. This user has never modified an EC2 instance before. +action.notable.param.rule_title = EC2 Instance Modified By Previously Unseen User $user$ +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 Instance Details by instanceId\n - ESCU - Get Notable History\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 = dest 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 = user, dest +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for EC2 instances being modified by users who have not previously modified them. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -961,7 +1045,7 @@ 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:cloudtrail `ec2ModificationAPIs` [search sourcetype=aws:cloudtrail `ec2ModificationAPIs` 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_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | 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] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest [ESCU - Remote Desktop Process Running On System - Rule] action.escu = 0 @@ -979,7 +1063,7 @@ action.escu.mappings = {"mitre_attack": ["Lateral Movement", "Remote Desktop Pro 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", "Tanium", "Ziften"] -action.escu.analytic_story = ["Hidden Cobra Malware", "Lateral Movement"] +action.escu.analytic_story = ["Lateral Movement", "Hidden Cobra Malware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Remote Desktop Process Running On System action.notable = 1 @@ -988,7 +1072,7 @@ action.notable.param.rule_description = The system $dest$ is running the remote action.notable.param.rule_title = Remote Desktop Process Running 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.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 @@ -1120,8 +1204,8 @@ action.escu.modification_date = 2019-02-14 action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU action.escu.confidence = medium -action.escu.eli5 = Using a lookup discover_dns_records generated by support search "Discover DNS records" we check previous network traffic and make sure the responses have not changed. -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 the discover_dns_record lookup table be populated by the included support search "Discover DNS record". \ +action.escu.eli5 = Using a lookup `discover_dns_records` generated by support search "Discover DNS records" we check previous network traffic and make sure the responses have not changed. +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 the `discover_dns_record` lookup table be populated by the included support search "Discover DNS record". \ \ **Splunk>Phantom Playbook Integration**\ \ @@ -1226,28 +1310,55 @@ schedule_window = auto is_visible = false search = sourcetype=stream:http http_content_type=text* | rex field=cookie "form_key=(?\w+)" | streamstats window=2 current=1 range(_time) as TimeDelta by session_id | where TimeDelta>0 |stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id | where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) -[ESCU - Discover DNS records] +[ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2019-02-14 -action.escu.modification_date = 2019-02-14 +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.eli5 = Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv` -action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation -action.escu.data_models = ["Network_Resolution"] -action.escu.full_search_name = ESCU - Discover DNS records -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 = ["DNS Hijacking"] -description = The search takes corporate and common cloud provider domains configured under cim_corporate_email_domains.csv, cim_corporate_web_domains.csv, and cloud_domains.csv and finds their responses across the last 30 days from data in the Network Traffic datamodel, then stores the output under the discovered_dns_records.csv lookup -dispatch.earliest_time = -30d@d +action.escu.confidence = medium +action.escu.eli5 = This search returns the number of times a URL associated with this type of JexBoss probe is observed. +action.escu.how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. +action.escu.data_models = ["Web"] +action.escu.full_search_name = ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule +action.escu.mappings = {"mitre_attack": ["Discovery", "System Information Discovery"], "kill_chain_phases": ["Reconnaissance"]} +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 = ["JBoss Vulnerability", "SamSam Ransomware"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect attackers scanning for vulnerable JBoss servers +action.notable = 1 +action.notable.param.nes_fields = +action.notable.param.rule_description = This search looks for specific GET/HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. +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 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 = 20 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest,url +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. +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 = | inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, "\*", "")) | join domain [|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records.csv +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") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `ctime(lastTime)` [ESCU - SMB Traffic Spike - Rule] action.escu = 0 @@ -1265,7 +1376,7 @@ action.escu.mappings = {"mitre_attack": ["Commonly Used Port"], "kill_chain_phas 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 = ["Emotet Malware (TA18-201A)", "Hidden Cobra Malware", "Ransomware", "DHS Report TA18-074A"] +action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "Ransomware", "Hidden Cobra Malware", "DHS Report TA18-074A"] action.correlationsearch.enabled = 1 action.correlationsearch.label = SMB Traffic Spike action.notable = 1 @@ -1299,6 +1410,57 @@ 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 +[ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] +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.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 +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 - Samsam Test File Write - Rule] action.escu = 0 action.escu.enabled = 1 @@ -1349,6 +1511,28 @@ 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_name) as file_name from datamodel=Endpoint.Filesystem where Filesystem.file_path=*\\windows\\system32\\test.txt by Filesystem.file_path | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)` +[ESCU - Previously Seen AWS Cross Account Activity] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-06-04 +action.escu.modification_date = 2018-06-04 +action.escu.channel = ESCU +action.escu.eli5 = In this support search, we look for **AssumeRole** events where the requesting account is different from the requested account. The first and last times these events are seen are written to 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. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. +action.escu.full_search_name = ESCU - Previously Seen AWS Cross Account Activity +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Cross Account Activity"] +description = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file. +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 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 - Investigate AWS activities via region name] action.escu = 0 action.escu.enabled = 1 @@ -1361,7 +1545,7 @@ action.escu.full_search_name = ESCU - Investigate AWS activities via region name 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 EC2 Activities", "Suspicious AWS S3 Activities"] +action.escu.analytic_story = ["Suspicious AWS S3 Activities", "Suspicious AWS EC2 Activities"] action.escu.fields_required = ["awsRegion"] action.escu.earliest_time_offset = 14400 action.escu.latest_time_offset = 0 @@ -1372,29 +1556,6 @@ 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 @@ -1493,56 +1654,6 @@ 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 = ["Prohibited Traffic Allowed or Protocol Mismatch", "Ransomware", "Command and Control"] -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 - 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 = 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 @@ -1655,7 +1766,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Splunk Enterprise Vulnerability", "Credential Dumping", "Suspicious MSHTA Activity", "DNS Amplification Attacks", "Monitor for Updates", "SamSam Ransomware", "Malicious PowerShell", "Asset Tracking", "Prohibited Traffic Allowed or Protocol Mismatch", "Suspicious AWS EC2 Activities", "Splunk Enterprise Vulnerability CVE-2018-11409", "Unusual AWS EC2 Modifications", "Emotet Malware (TA18-201A)", "Use of Cleartext Protocols", "Apache Struts Vulnerability", "Windows Persistence Techniques", "Account Monitoring and Controls", "Suspicious AWS S3 Activities", "Hidden Cobra Malware", "Ransomware", "Suspicious Windows Registry Activities", "Suspicious Emails", "AWS Cross Account Activity", "DNS Hijacking", "Windows Log Manipulation", "Lateral Movement", "Windows Service Abuse", "Netsh Abuse", "Windows Defense Evasion Tactics", "Dynamic DNS", "Host Redirection", "Monitor Backup Solution", "Spectre And Meltdown Vulnerabilities", "Command and Control", "JBoss Vulnerability", "Monitor for Unauthorized Software", "Brand Monitoring", "Suspicious WMI Use", "Suspicious AWS Login Activities", "AWS Network ACL Activity", "Router & Infrastructure Security", "Orangeworm Attack Group", "Suspicious DNS Traffic", "AWS User Monitoring", "Web Fraud Detection", "Windows Privilege Escalation", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "Unusual Processes", "SQL Injection", "ColdRoot MacOS RAT", "Collection and Staging", "Disabling Security Tools", "Data Protection"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Splunk Enterprise Vulnerability", "Credential Dumping", "SQL Injection", "Suspicious WMI Use", "Lateral Movement", "Disabling Security Tools", "AWS Network ACL Activity", "Netsh Abuse", "Emotet Malware (TA18-201A)", "Web Fraud Detection", "Account Monitoring and Controls", "Windows Persistence Techniques", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "JBoss Vulnerability", "Suspicious AWS S3 Activities", "Windows Defense Evasion Tactics", "Splunk Enterprise Vulnerability CVE-2018-11409", "Dynamic DNS", "Unusual Processes", "Brand Monitoring", "Suspicious AWS EC2 Activities", "Suspicious MSHTA Activity", "Windows Log Manipulation", "Windows Privilege Escalation", "Asset Tracking", "AWS Cross Account Activity", "Suspicious AWS Login Activities", "ColdRoot MacOS RAT", "Suspicious DNS Traffic", "Command and Control", "SamSam Ransomware", "DNS Amplification Attacks", "Router & Infrastructure Security", "Monitor for Unauthorized Software", "Collection and Staging", "Windows Service Abuse", "Suspicious Windows Registry Activities", "Host Redirection", "Suspicious Emails", "Ransomware", "Hidden Cobra Malware", "Spectre And Meltdown Vulnerabilities", "Apache Struts Vulnerability", "Monitor for Updates", "Suspicious AWS Traffic", "AWS User Monitoring", "Windows File Extension and Association Abuse", "Malicious PowerShell", "Data Protection", "Unusual AWS EC2 Modifications", "Monitor Backup Solution", "DHS Report TA18-074A", "Use of Cleartext Protocols", "DNS Hijacking", "Prohibited Traffic Allowed or Protocol Mismatch"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 864000 action.escu.latest_time_offset = 86400 @@ -1678,7 +1789,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Credential Dumping", "Suspicious MSHTA Activity", "SamSam Ransomware", "Emotet Malware (TA18-201A)", "Windows Persistence Techniques", "Hidden Cobra Malware", "Ransomware", "Suspicious Windows Registry Activities", "Windows Service Abuse", "Netsh Abuse", "Windows Defense Evasion Tactics", "Orangeworm Attack Group", "Windows Privilege Escalation", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "Collection and Staging", "Disabling Security Tools"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Credential Dumping", "Disabling Security Tools", "Netsh Abuse", "Emotet Malware (TA18-201A)", "Windows Persistence Techniques", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "Windows Defense Evasion Tactics", "Suspicious MSHTA Activity", "Windows Privilege Escalation", "SamSam Ransomware", "Collection and Staging", "Windows Service Abuse", "Suspicious Windows Registry Activities", "Ransomware", "Hidden Cobra Malware", "Windows File Extension and Association Abuse", "DHS Report TA18-074A"] action.escu.fields_required = ["process", "dest"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 86400 @@ -1705,7 +1816,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface", action.escu.known_false_positives = Some VPN applications are known to launch netsh.exe. Outside of these instances, it is unusual for an executable to launch netsh.exe and run commands. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Netsh Abuse", "DHS Report TA18-074A", "Disabling Security Tools"] +action.escu.analytic_story = ["Disabling Security Tools", "Netsh Abuse", "DHS Report TA18-074A"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Processes launching netsh action.notable = 1 @@ -1789,46 +1900,45 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Application_State where All_Application_State.dest_category="web_server" AND (All_Application_State.process="*whoami*" OR All_Application_State.process="*ping*" OR All_Application_State.process="*iptables*" OR All_Application_State.process="*wget*" OR All_Application_State.process="*service*" OR All_Application_State.process="*curl*") by All_Application_State.process, All_Application_State.dest | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name("All_Application_State")` -[ESCU - Large Volume of DNS ANY Queries - Rule] +[ESCU - Schtasks used for forcing a reboot - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-08-24 -action.escu.modification_date = 2017-09-20 -action.escu.asset_at_risk = DNS Servers +action.escu.creation_date = 2017-11-03 +action.escu.modification_date = 2017-11-03 +action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set. -action.escu.how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. -action.escu.data_models = ["Network_Resolution"] -action.escu.full_search_name = ESCU - Large Volume of DNS ANY Queries - Rule -action.escu.mappings = {"kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11", "CIS 12"], "nist": ["PR.PT", "DE.AE", "PR.IP"]} -action.escu.known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. +action.escu.confidence = medium +action.escu.eli5 = The search looks for execution of schtasks.exe with parameters that indicate a task is being scheduled that would cause a forced reboot on the 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 establish persistence. This tactic is leveraged by the Bad Rabbit Ransomware. +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 used for forcing a reboot - Rule +action.escu.mappings = {"mitre_attack": ["Persistence", "Execution", "Scheduled Task"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} +action.escu.known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. action.escu.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["DNS Amplification Attacks"] +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Windows Persistence Techniques", "Ransomware"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Large Volume of DNS ANY Queries +action.correlationsearch.label = Schtasks used for forcing a reboot action.notable = 1 -action.notable.param.nes_fields = dest -action.notable.param.rule_description = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. -action.notable.param.rule_title = Large Volume of DNS ANY Queries -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 Notable Info\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"} +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 scheduled to force a reboot +action.notable.param.rule_title = Schtasks used for scheduling a force reboot +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 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 action.risk.param._risk_object_type = system -action.risk.param._risk_score = 60 +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 = 7200s -cron_schedule = */5 * * * * -description = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. -dispatch.earliest_time = -15m@m -dispatch.latest_time = -10m@m +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 that a forced reboot of system is scheduled. +dispatch.earliest_time = -5h@h +dispatch.latest_time = -1h@h disabled=true enableSched = 1 counttype = number of events @@ -1837,7 +1947,7 @@ quantity = 0 realtime_schedule = 0 schedule_window = auto is_visible = false -search = | tstats `summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 +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 - Previously Seen EC2 Launches By User] action.escu = 0 @@ -1851,7 +1961,7 @@ action.escu.full_search_name = ESCU - Previously Seen EC2 Launches By User 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"] +action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] description = This search builds a table of previously seen ARNs that have launched a EC2 instance. dispatch.earliest_time = -90d@d dispatch.latest_time = -10m@m @@ -1861,44 +1971,43 @@ 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 Long DNS TXT Record Response - Rule] +[ESCU - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] 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 = 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 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.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 = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["Command and Control", "Suspicious DNS Traffic"] +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Credential Dumping"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Long DNS TXT Record Response +action.correlationsearch.label = Detect Mimikatz Via PowerShell And EventCode 4663 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.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 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 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 = src +action.risk.param._risk_object = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 70 +action.risk.param._risk_score = 40 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = src +alert.suppress.fields = user, dest, process 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. +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 @@ -1909,7 +2018,7 @@ 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=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=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 - Suspicious Reg.exe Process - Rule] action.escu = 0 @@ -1926,7 +2035,7 @@ 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 = ["Windows Defense Evasion Tactics", "DHS Report TA18-074A", "Disabling Security Tools"] +action.escu.analytic_story = ["Disabling Security Tools", "Windows Defense Evasion Tactics", "DHS Report TA18-074A"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Suspicious Reg.exe Process action.notable = 1 @@ -2010,27 +2119,55 @@ 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.parent_process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)`| search (process=*add* process=*Software\\Microsoft\\Powershell\\1\\ShellIds\\Microsoft.PowerShell* process=*ExecutionPolicy* process=*Unrestricted*) -[ESCU - Previously Seen AWS Cross Account Activity] +[ESCU - Prohibited Network Traffic Allowed - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-06-04 -action.escu.modification_date = 2018-06-04 +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 look for **AssumeRole** events where the requesting account is different from the requested account. The first and last times these events are seen are written to 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. Validate the user name entries in `previously_seen_aws_cross_account_activity.csv`, a lookup file created by this support search. -action.escu.full_search_name = ESCU - Previously Seen AWS Cross Account Activity -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS Cross Account Activity"] -description = This search looks for **AssumeRole** events where the requesting account differs from the requested account, then writes these relationships to a lookup file. -dispatch.earliest_time = -30d@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 = ["Command and Control", "Ransomware", "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=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 +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 - Suspicious writes to windows Recycle Bin - Rule] action.escu = 0 @@ -2081,77 +2218,50 @@ 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)` -[ESCU - Detect API activity from users without MFA - Rule] +[ESCU - Get Logon Rights Modifications For Endpoint] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-05-17 -action.escu.modification_date = 2018-05-17 -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 and specifically look for events where the multi factor authentication context of the user's session is false which basically means, that the user does not have MFA enabled on AWS. We then filter out all the known AWS service accounts since service accounts typically do not have MFA enabled. The search then creates a table of the first and last time a user without MFA was detected, the values and count of the API calls made, the type of user identity, ARN and the name of 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. Leverage the support search `Create a list of approved AWS service accounts`: run it once every 30 days to create a list of service accounts and validate them. -action.escu.full_search_name = ESCU - Detect API activity from users without MFA - Rule -action.escu.mappings = {"mitre_attack": ["Execution"], "cis20": ["CIS 16"], "nist": ["DE.DP", "PR.AC"]} -action.escu.known_false_positives = Many service accounts configured within an AWS infrastructure do not have multi factor authentication enabled. Please ignore the service accounts, if triggered and instead add them to the aws_service_accounts.csv file to fine tune the detection. It is also possible that the search detects users in your environment using Single Sign-On systems, since the MFA is not handled by AWS. -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 API activity from users without MFA -action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = API Activity detected from $user$ without MFA enabled. -action.notable.param.rule_title = API Activity detected from $user$ without MFA enabled -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.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 = 84600s -cron_schedule = 0 8 * * * -description = This search looks for CloudTrail events where a user logged into the AWS account, is making API calls and has not enabled Multi Factor authentication. Multi factor authentication adds a layer of security by forcing the users to type a unique authentication code from an approved authentication device when they access AWS websites or services. AWS Best Practices recommend that you enable MFA for privileged IAM users. -dispatch.earliest_time = -1d@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 userIdentity.sessionContext.attributes.mfaAuthenticated=false | search NOT [| inputlookup aws_service_accounts | fields identity | rename identity as user]| stats count min(_time) as firstTime max(_time) as lastTime values(eventName) by userIdentity.arn userIdentity.type user | `ctime(firstTime)` | `ctime(lastTime)` - -[ESCU - All backup logs for host] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-06-19 +action.escu.creation_date = 2017-08-16 action.escu.modification_date = 2017-09-12 action.escu.channel = ESCU action.escu.eli5 = none -action.escu.how_to_implement = The successfully implement this search you must first send your backup logs to Splunk. -action.escu.full_search_name = ESCU - All backup logs for host +action.escu.how_to_implement = To successfully implement this search you must be ingesting your Windows event logs +action.escu.full_search_name = ESCU - Get Logon Rights Modifications For Endpoint action.escu.known_false_positives = None at this time action.escu.search_type = investigative -action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["Monitor Backup Solution"] +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Account Monitoring and Controls"] action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 1209600 -action.escu.latest_time_offset = 0 -description = Retrieve the backup logs for the last 2 weeks for a specific host in order to investigate why backups are not completing successfully. +action.escu.earliest_time_offset = 86400 +action.escu.latest_time_offset = 86400 +description = This search allows you to retrieve any modifications to logon rights associated with a specific host. disabled=true realtime_schedule = 0 schedule_window = auto is_visible = false -search = | search sourcetype="netbackup_logs" dest={dest} +search = | search sourcetype=WinEventLog:Security (EventCode=4718 OR EventCode=4717) dest={dest} | rename user as "Account Modified" | table _time, dest, "Account Modified", Access_Right, signature + +[ESCU - Previously Seen Running Windows Services] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-07-20 +action.escu.modification_date = 2018-07-20 +action.escu.channel = ESCU +action.escu.eli5 = In this support search, we look for Windows system-event code that indicates a status change of a Windows service. It extracts both the name of the service and the action taken by the service from the logs. It keeps only services that have entered the running state. Finally, it finds the first time the service has been seen running across the enterprise and writes that file to a lookup table. +action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. +action.escu.full_search_name = ESCU - Previously Seen Running Windows Services +action.escu.known_false_positives = None at this time +action.escu.search_type = support +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Orangeworm Attack Group", "Windows Service Abuse"] +description = This collects the services that have been started across your entire enterprise. +dispatch.earliest_time = -30d@d +dispatch.latest_time = -10m@m +disabled=true +realtime_schedule = 0 +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 - Clients Connecting to Multiple DNS Servers - Rule] action.escu = 0 @@ -2169,7 +2279,7 @@ 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 = ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control", "DNS Hijacking"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Clients Connecting to Multiple DNS Servers action.notable = 1 @@ -2178,7 +2288,7 @@ 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 - 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 - ESCU - Get Vulnerability Logs For Endpoint\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 History Of Email Sources\n - ESCU - Get Process responsible for the DNS traffic\n - ESCU - Investigate Web Activity From src_ip\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = src @@ -2203,6 +2313,29 @@ 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 @@ -2253,151 +2386,50 @@ 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] +[ESCU - Previously Seen AWS Regions] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-10-23 -action.escu.modification_date = 2018-10-23 +action.escu.creation_date = 2018-01-08 +action.escu.modification_date = 2018-01-08 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.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 = 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 - 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 Command-Line Executions", "Suspicious MSHTA Activity"] -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 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.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, parent_process -alert.suppress.period = 86400s -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. -dispatch.earliest_time = -70m@m +action.escu.search_type = support +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] +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 -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.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_name Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] +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 USB device insertion - Rule] +[ESCU - Get User Information from Identity Table] action.escu = 0 action.escu.enabled = 1 -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 = 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 = ["Microsoft Windows"] -action.escu.analytic_story = ["Data Protection"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect USB device insertion -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.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.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 -alert.suppress.period = 86400s -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. -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 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 - Get Backup Logs For Endpoint] -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 = 2017-04-10 +action.escu.modification_date = 2017-09-20 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.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 = ["Netbackup"] -action.escu.analytic_story = ["SamSam Ransomware", "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.providing_technologies = ["Splunk Enterprise Security"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Splunk Enterprise Vulnerability", "Credential Dumping", "SQL Injection", "Suspicious WMI Use", "Lateral Movement", "Disabling Security Tools", "AWS Network ACL Activity", "Netsh Abuse", "Emotet Malware (TA18-201A)", "Account Monitoring and Controls", "Windows Persistence Techniques", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "JBoss Vulnerability", "Suspicious AWS S3 Activities", "Windows Defense Evasion Tactics", "Dynamic DNS", "Unusual Processes", "Brand Monitoring", "Suspicious AWS EC2 Activities", "Suspicious MSHTA Activity", "Windows Log Manipulation", "Windows Privilege Escalation", "Asset Tracking", "Suspicious AWS Login Activities", "ColdRoot MacOS RAT", "Suspicious DNS Traffic", "Command and Control", "SamSam Ransomware", "Router & Infrastructure Security", "Monitor for Unauthorized Software", "Collection and Staging", "Windows Service Abuse", "Suspicious Windows Registry Activities", "Host Redirection", "Suspicious Emails", "Ransomware", "Hidden Cobra Malware", "Spectre And Meltdown Vulnerabilities", "Apache Struts Vulnerability", "Monitor for Updates", "Windows File Extension and Association Abuse", "Malicious PowerShell", "Data Protection", "DHS Report TA18-074A", "Use of Cleartext Protocols", "DNS Hijacking"] +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 = | search sourcetype="netbackup_logs" COMPUTERNAME={dest} | rename COMPUTERNAME as dest, MESSAGE as signature | table _time, dest, signature +search = | `identities` | search identity={user} | table _time, identity, first, last, email, category, watchlist [ESCU - Get DNS Server History for a host] action.escu = 0 @@ -2411,7 +2443,7 @@ 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 = ["DNS Hijacking", "Dynamic DNS", "Host Redirection", "Command and Control", "Brand Monitoring", "Suspicious DNS Traffic", "Data Protection"] +action.escu.analytic_story = ["Dynamic DNS", "Brand Monitoring", "Suspicious DNS Traffic", "Command and Control", "Host Redirection", "Data Protection", "DNS Hijacking"] action.escu.fields_required = ["src_ip"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 86400 @@ -2472,54 +2504,27 @@ schedule_window = auto is_visible = false search = | from datamodel Alerts.Alerts | search app=osquery:results (name=pack_osx-attacks_OSX_ColdRoot_RAT_Launchd OR name=pack_osx-attacks_OSX_ColdRoot_RAT_Files) | rename columns.path as path | bucket _time span=30s | stats count(path) by _time, host, user, path -[ESCU - Unsuccessful Netbackup backups - Rule] +[ESCU - Previously Seen EC2 Instance Types] 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 = 2018-03-08 +action.escu.modification_date = 2018-03-08 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.search_type = detection -action.escu.providing_technologies = ["Netbackup"] -action.escu.analytic_story = ["Monitor Backup Solution"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Unsuccessful Netbackup backups -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.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 = 10 -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 +action.escu.eli5 = In this support search, we create a table of the earliest and latest time that a specific EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. 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 Instance Types +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 EC2 instance types +dispatch.earliest_time = -90d@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="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 +search = sourcetype=aws:cloudtrail eventName=RunInstances errorCode=success | rename requestParameters.instanceType as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | stats count [ESCU - Remote Registry Key modifications - Rule] action.escu = 0 @@ -2537,7 +2542,7 @@ 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 = ["Windows Persistence Techniques", "Suspicious Windows Registry Activities", "Lateral Movement", "Windows Defense Evasion Tactics"] +action.escu.analytic_story = ["Lateral Movement", "Windows Persistence Techniques", "Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Remote Registry Key modifications action.notable = 1 @@ -2546,7 +2551,7 @@ action.notable.param.rule_description = A registry key was modified remotely usi action.notable.param.rule_title = Remote Registry Key Modification detection 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.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.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -2571,44 +2576,45 @@ 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="\\\\*" by Registry.dest , Registry.status, Registry.user | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name(Registry)` -[ESCU - Abnormally High AWS Instances Launched by User - Rule] +[ESCU - Detect malicious requests to exploit JBoss servers - 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 = 2016-10-04 +action.escu.modification_date = 2017-09-23 +action.escu.asset_at_risk = Web Server 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.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.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Suspicious AWS EC2 Activities", "AWS Cryptomining"] +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 = Abnormally High AWS Instances Launched by User +action.correlationsearch.label = Detect malicious requests to exploit JBoss servers 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.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 = 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.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 = userName -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 = 80 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 +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 @@ -2618,7 +2624,7 @@ 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 - Get Notable Info] action.escu = 0 @@ -2632,7 +2638,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious AWS Traffic", "Suspicious Command-Line Executions", "Splunk Enterprise Vulnerability", "Credential Dumping", "DNS Amplification Attacks", "Monitor for Updates", "Malicious PowerShell", "Asset Tracking", "Suspicious AWS EC2 Activities", "Splunk Enterprise Vulnerability CVE-2018-11409", "Emotet Malware (TA18-201A)", "Use of Cleartext Protocols", "Apache Struts Vulnerability", "Windows Persistence Techniques", "Account Monitoring and Controls", "Suspicious AWS S3 Activities", "Hidden Cobra Malware", "Windows Log Manipulation", "Lateral Movement", "Windows Service Abuse", "Windows Defense Evasion Tactics", "Dynamic DNS", "Host Redirection", "Spectre And Meltdown Vulnerabilities", "Command and Control", "JBoss Vulnerability", "Brand Monitoring", "Suspicious WMI Use", "Suspicious AWS Login Activities", "AWS Network ACL Activity", "Router & Infrastructure Security", "Orangeworm Attack Group", "Suspicious DNS Traffic", "AWS User Monitoring", "Web Fraud Detection", "Windows Privilege Escalation", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "SQL Injection", "Collection and Staging", "Disabling Security Tools", "Data Protection"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Splunk Enterprise Vulnerability", "Credential Dumping", "SQL Injection", "Suspicious WMI Use", "Lateral Movement", "Disabling Security Tools", "AWS Network ACL Activity", "Emotet Malware (TA18-201A)", "Web Fraud Detection", "Account Monitoring and Controls", "Windows Persistence Techniques", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "JBoss Vulnerability", "Suspicious AWS S3 Activities", "Windows Defense Evasion Tactics", "Splunk Enterprise Vulnerability CVE-2018-11409", "Dynamic DNS", "Brand Monitoring", "Suspicious AWS EC2 Activities", "Windows Log Manipulation", "Windows Privilege Escalation", "Asset Tracking", "Suspicious AWS Login Activities", "Suspicious DNS Traffic", "Command and Control", "DNS Amplification Attacks", "Router & Infrastructure Security", "Collection and Staging", "Windows Service Abuse", "Host Redirection", "Hidden Cobra Malware", "Spectre And Meltdown Vulnerabilities", "Apache Struts Vulnerability", "Monitor for Updates", "Suspicious AWS Traffic", "AWS User Monitoring", "Windows File Extension and Association Abuse", "Malicious PowerShell", "Data Protection", "DHS Report TA18-074A", "Use of Cleartext Protocols"] action.escu.fields_required = ["event_id"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 3600 @@ -2659,7 +2665,7 @@ 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 @@ -2668,7 +2674,7 @@ 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 @@ -2759,7 +2765,7 @@ 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 = ["Hidden Cobra Malware", "Command and Control", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control", "Hidden Cobra Malware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = DNS Query Length With High Standard Deviation action.notable = 1 @@ -2768,7 +2774,7 @@ 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 - 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 @@ -2809,7 +2815,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Indicator Removal o 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 = ["Ransomware", "Windows Log Manipulation"] +action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = USN Journal Deletion action.notable = 1 @@ -2818,7 +2824,7 @@ action.notable.param.rule_description = The system $dest$ deleted its NTFS journ action.notable.param.rule_title = File System Journal 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 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 Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -2843,27 +2849,52 @@ 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 - Previously Seen AWS Regions] +[ESCU - Identify Systems Creating Remote Desktop Traffic] 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 = 2017-04-24 +action.escu.modification_date = 2017-09-15 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 = 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. +action.escu.how_to_implement = To successfully implement this search, you must ingest network traffic and populate the Network_Traffic data model. +action.escu.data_models = ["Network_Traffic"] +action.escu.full_search_name = ESCU - Identify Systems Creating Remote Desktop Traffic 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.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["Lateral Movement"] +description = This search counts the numbers of times the system has generated remote desktop traffic. 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 +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 Process responsible for the DNS traffic] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-04-10 +action.escu.modification_date = 2017-11-09 +action.escu.channel = ESCU +action.escu.eli5 = none +action.escu.how_to_implement = You must be ingesting endpoint data that associates processes with network events. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon. +action.escu.data_models = ["Application_State"] +action.escu.full_search_name = ESCU - Get Process responsible for the DNS traffic +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 = ["Dynamic DNS", "Brand Monitoring", "Suspicious DNS Traffic", "Command and Control", "Host Redirection", "Data Protection", "DNS Hijacking"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 3600 +action.escu.latest_time_offset = 86400 +description = While investigating, an analyst will want to know what process and parent_ process is responsible for generating suspicious DNS traffic. Use the following search and enter the value of src_ip in the search to get specific details on the process responsible for creating the DNS traffic. +disabled=true +realtime_schedule = 0 +schedule_window = auto +is_visible = false +search = | tstats allow_old_summaries=true values(All_Application_State.process) as "process" from datamodel=Application_State where nodename=All_Application_State.Ports All_Application_State.Ports.dest_port=53 All_Application_State.dest={dest} [ESCU - WMI Temporary Event Subscription - Rule] action.escu = 0 @@ -2930,7 +2961,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Modify Registry"], action.escu.known_false_positives = This registry key may be modified via administrators to implement a change in system policy. This type of change should be a very rare occurrence. 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"] +action.escu.analytic_story = ["Windows Defense Evasion Tactics", "Suspicious Windows Registry Activities"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Disabling Remote User Account Control action.notable = 1 @@ -2939,7 +2970,7 @@ action.notable.param.rule_description = The registry key SOFTWARE\Microsoft\Wind action.notable.param.rule_title = Registry Key Associated With Disabling Remote UAC Modified 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.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.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -2964,55 +2995,6 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path="*Windows\\CurrentVersion\\Policies\\System\\LocalAccountTokenFilterPolicy" by Registry.dest, Registry.registry_key_name Registry.status Registry.user Registry.registry_path Registry.action | `drop_dm_object_name(Registry)` -[ESCU - EC2 Instance Started In Previously Unseen Region - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-02-01 -action.escu.modification_date = 2018-02-23 -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 that indicate that an instance was started in a particular region. Using the `previously_seen_aws_regions.csv` lookup file created using the support search, we compare the region where this instance was started to all previously observed regions. The `eval` and `if` functions determine that the earliest times seen for this region and instance were within the last day. If a new region is detected, it will alert you with "Instance Started in a New Region". However, this region will be added to the list of `previously_seen_aws_regions.csv`. Please maintain `previously_seen_aws_regions.csv` -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 Regions" support search only once to create of baseline of previously seen regions. -action.escu.full_search_name = ESCU - EC2 Instance Started In Previously Unseen Region - Rule -action.escu.mappings = {"mitre_attack": ["Defense Evasion"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 12"], "nist": ["DE.DP", "DE.AE"]} -action.escu.known_false_positives = It's possible that a user has unknowingly started an instance in a new region. Please verify that this activity is legitimate. -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 = EC2 Instance Started In Previously Unseen Region -action.notable = 1 -action.notable.param.nes_fields = awsRegion -action.notable.param.rule_description = An AWS instance is started in a new, previously unseen, region -action.notable.param.rule_title = AWS instance is started in a new region -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 = awsRegion -action.risk.param._risk_object_type = other -action.risk.param._risk_score = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = awsRegion -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for CloudTrail events where an instance is started in a particular region in the last one hour and then compares it to a lookup file of previously seen regions where an instance was started -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 earliest=-1h StartInstances | stats earliest(_time) as earliest latest(_time) as latest by awsRegion| inputlookup append=t previously_seen_aws_regions.csv | stats min(earliest) as earliest max(latest) as latest by awsRegion | outputlookup previously_seen_aws_regions.csv | eval regionStatus=if(earliest >= relative_time(now(), "-1d@d"), "Instance Started in a New Region","Previously Seen Region") | convert ctime(earliest) ctime(latest) | where regionStatus="Instance Started in a New Region" - [ESCU - AWS Cloud Provisioning From Previously Unseen City - Rule] action.escu = 0 action.escu.enabled = 1 @@ -3128,7 +3110,7 @@ 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 = ["Suspicious Command-Line Executions", "Unusual Processes", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = System Processes Run From Unexpected Locations action.notable = 1 @@ -3137,7 +3119,7 @@ action.notable.param.rule_description = The system $dest$ has a process that nor action.notable.param.rule_title = System Processes Run From Unexpected Location 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 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 Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -3162,44 +3144,61 @@ 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 - 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 @@ -3210,7 +3209,7 @@ 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 S3 bucket access by remote IP] action.escu = 0 @@ -3234,56 +3233,6 @@ schedule_window = auto is_visible = false search = sourcetype=aws:s3:accesslogs http_status=200 | stats earliest(_time) as earliest latest(_time) as latest by bucket_name remote_ip | outputlookup previously_seen_S3_access_from_remote_ip | stats count -[ESCU - Execution of File with Multiple Extensions - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-01-26 -action.escu.modification_date = 2018-11-02 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search uses the "Application State" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the "process" field that match what you're looking for. -action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. -action.escu.data_models = ["Endpoint"] -action.escu.full_search_name = ESCU - Execution of File with Multiple Extensions - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 8"], "nist": ["DE.CM", "PR.PT", "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 = ["Windows File Extension and Association Abuse"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Execution of File with Multiple Extensions -action.notable = 1 -action.notable.param.nes_fields = dest, process -action.notable.param.rule_description = The system $dest$ executed a file with a double extension. -action.notable.param.rule_title = Process With Multiple Extensions Launched 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 = 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 -alert.suppress.period = 28800s -cron_schedule = 0 * * * * -description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. -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.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(Processes)` - [ESCU - Common Ransomware Notes - Rule] action.escu = 0 action.escu.enabled = 1 @@ -3496,7 +3445,7 @@ 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 = ["Suspicious Emails", "Brand Monitoring", "Web Fraud Detection"] +action.escu.analytic_story = ["Web Fraud Detection", "Brand Monitoring", "Suspicious Emails"] action.escu.fields_required = ["src_user"] action.escu.earliest_time_offset = 86400 action.escu.latest_time_offset = 86400 @@ -3630,27 +3579,54 @@ 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_hash) as file_hash values(Filesystem.file_path) as file_path from datamodel=Endpoint.Filesystem where Filesystem.file_path="C:\\program.exe" by Filesystem.file_name | `drop_dm_object_name(Filesystem)` | `ctime(lastTime)` | `ctime(firstTime)` -[ESCU - Previously Seen EC2 Instance Types] +[ESCU - First time seen command line argument - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-08 -action.escu.modification_date = 2018-03-08 +action.escu.creation_date = 2018-04-09 +action.escu.modification_date = 2018-04-16 +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 EC2 instance type has been seen. The instanceType request field is not required and defaults to m1.small, so any time this field is null, the search defaults the field to m1.small. 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 Instance Types -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 EC2 instance types -dispatch.earliest_time = -90d@d +action.escu.confidence = medium +action.escu.eli5 = The subsearch returns all events where `cmd.exe` was used with a `/c` parameter in the command-line arguments to execute other commands/programs. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past four hours. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument. +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 Technology Add-on (TA). Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. +action.escu.full_search_name = ESCU - First time seen command line argument - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Scripting", "Persistence", "Command-Line Interface"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} +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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "Hidden Cobra Malware", "DHS Report TA18-074A"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = First time seen command line argument +action.notable = 1 +action.notable.param.nes_fields = dest, user, process, cmdline +action.notable.param.rule_description = The system $dest$ executed a command-line argument, $cmdline$, that has not previously been seen. +action.notable.param.rule_title = First-time seen command-line argument was 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.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, cmdline +alert.suppress.period = 86400s +cron_schedule = 30 * * * * +description = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. +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.instanceType as instanceType | fillnull value="m1.small" instanceType | stats earliest(_time) as earliest latest(_time) as latest by instanceType | outputlookup previously_seen_ec2_instance_types.csv | stats count +search = sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational process=cmd.exe cmdline="* /c *" [ search sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational process=cmd.exe cmdline="* /c *" | stats earliest(_time) as firstTime latest(_time) as lastTime by cmdline | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by cmdline | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `ctime(firstTime)` | `ctime(lastTime)` | table cmdline] | table _time, user,dest, process, parent_process, cmdline [ESCU - Suspicious Email Attachment Extensions - Rule] action.escu = 0 @@ -3786,7 +3762,7 @@ 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 = ["Suspicious AWS Traffic", "Suspicious AWS S3 Activities", "Command and Control", "AWS Suspicious Provisioning Activities"] +action.escu.analytic_story = ["Suspicious AWS S3 Activities", "Command and Control", "Suspicious AWS Traffic", "AWS Suspicious Provisioning Activities"] action.escu.fields_required = ["src_ip"] action.escu.earliest_time_offset = 14400 action.escu.latest_time_offset = 0 @@ -3797,50 +3773,51 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:cloudtrail | iplocation sourceIPAddress | search sourceIPAddress={src_ip} | 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, user, userName, userType, src_ip, awsRegion, eventName, errorCode -[ESCU - Previously Seen Running Windows Services] +[ESCU - All backup logs for host] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-07-20 -action.escu.modification_date = 2018-07-20 -action.escu.channel = ESCU -action.escu.eli5 = In this support search, we look for Windows system-event code that indicates a status change of a Windows service. It extracts both the name of the service and the action taken by the service from the logs. It keeps only services that have entered the running state. Finally, it finds the first time the service has been seen running across the enterprise and writes that file to a lookup table. -action.escu.how_to_implement = While this search does not require you to adhere to Splunk CIM, you must be ingesting your Windows security-event logs for it to execute successfully. -action.escu.full_search_name = ESCU - Previously Seen Running Windows Services -action.escu.known_false_positives = None at this time -action.escu.search_type = support -action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Windows Service Abuse", "Orangeworm Attack Group"] -description = This collects the services that have been started across your entire enterprise. -dispatch.earliest_time = -30d@d -dispatch.latest_time = -10m@m -disabled=true -realtime_schedule = 0 -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 User Information from Identity Table] -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 = 2017-06-19 +action.escu.modification_date = 2017-09-12 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 = The successfully implement this search you must first send your backup logs to Splunk. +action.escu.full_search_name = ESCU - All backup logs for host 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Splunk Enterprise Vulnerability", "Credential Dumping", "Suspicious MSHTA Activity", "Monitor for Updates", "SamSam Ransomware", "Malicious PowerShell", "Asset Tracking", "Suspicious AWS EC2 Activities", "Emotet Malware (TA18-201A)", "Use of Cleartext Protocols", "Apache Struts Vulnerability", "Windows Persistence Techniques", "Account Monitoring and Controls", "Suspicious AWS S3 Activities", "Hidden Cobra Malware", "Ransomware", "Suspicious Windows Registry Activities", "Suspicious Emails", "DNS Hijacking", "Windows Log Manipulation", "Lateral Movement", "Windows Service Abuse", "Netsh Abuse", "Windows Defense Evasion Tactics", "Dynamic DNS", "Host Redirection", "Spectre And Meltdown Vulnerabilities", "Command and Control", "JBoss Vulnerability", "Monitor for Unauthorized Software", "Brand Monitoring", "Suspicious WMI Use", "Suspicious AWS Login Activities", "AWS Network ACL Activity", "Router & Infrastructure Security", "Orangeworm Attack Group", "Suspicious DNS Traffic", "Windows Privilege Escalation", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "Unusual Processes", "SQL Injection", "ColdRoot MacOS RAT", "Collection and Staging", "Disabling Security Tools", "Data Protection"] -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 = ["Netbackup"] +action.escu.analytic_story = ["Monitor Backup Solution"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 1209600 +action.escu.latest_time_offset = 0 +description = Retrieve the backup logs for the last 2 weeks for a specific host in order to investigate why backups are not completing successfully. 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="netbackup_logs" dest={dest} + +[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 - Child Processes of Spoolsv.exe - Rule] action.escu = 0 @@ -3892,52 +3869,55 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count values(Processes.process_name) as process_name values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=spoolsv.exe AND Processes.process_name!=regsvr32.exe by Processes.dest Processes.parent_process Processes.user | `drop_dm_object_name(Processes)` | `ctime(firstTime)` | `ctime(lastTime)` -[ESCU - Get Process responsible for the DNS traffic] +[ESCU - Detect Prohibited Applications Spawning cmd.exe - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-04-10 -action.escu.modification_date = 2017-11-09 +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.eli5 = none -action.escu.how_to_implement = You must be ingesting endpoint data that associates processes with network events. This can come from endpoint protection products such as carbon black, or endpoint data sources such as Sysmon. -action.escu.data_models = ["Application_State"] -action.escu.full_search_name = ESCU - Get Process responsible for the DNS traffic -action.escu.known_false_positives = None at this time -action.escu.search_type = investigative +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 = ["DNS Hijacking", "Dynamic DNS", "Host Redirection", "Command and Control", "Brand Monitoring", "Suspicious DNS Traffic", "Data Protection"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 3600 -action.escu.latest_time_offset = 86400 -description = While investigating, an analyst will want to know what process and parent_ process is responsible for generating suspicious DNS traffic. Use the following search and enter the value of src_ip in the search to get specific details on the process responsible for creating the DNS traffic. -disabled=true -realtime_schedule = 0 -schedule_window = auto -is_visible = false -search = | tstats allow_old_summaries=true values(All_Application_State.process) as "process" from datamodel=Application_State where nodename=All_Application_State.Ports All_Application_State.Ports.dest_port=53 All_Application_State.dest={dest} - -[ESCU - Systems Ready for Spectre-Meltdown Windows Patch] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-01-08 -action.escu.modification_date = 2018-01-08 -action.escu.channel = ESCU -action.escu.eli5 = This search looks to see if a registry key was created at `HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat`. It will tell you when it was created and, if possible, what process created it. -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.data_models = ["Change_Analysis"] -action.escu.full_search_name = ESCU - Systems Ready for Spectre-Meltdown Windows Patch -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 = ["Spectre And Meltdown Vulnerabilities"] -description = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown. -dispatch.earliest_time = -1d@d +action.escu.analytic_story = ["Suspicious Command-Line Executions", "Suspicious MSHTA Activity"] +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 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.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, parent_process +alert.suppress.period = 86400s +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. +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="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name("All_Changes")` +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_name Processes.process_name Processes.dest | `drop_dm_object_name(Processes)` | `ctime(firstTime)`| `ctime(lastTime)` |search [`prohibited_apps_launching_cmd`] [ESCU - Detect hosts connecting to dynamic domain providers - Rule] action.escu = 0 @@ -3955,7 +3935,7 @@ 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 = ["Prohibited Traffic Allowed or Protocol Mismatch", "DNS Hijacking", "Dynamic DNS", "Command and Control", "Suspicious DNS Traffic", "Data Protection"] +action.escu.analytic_story = ["Dynamic DNS", "Suspicious DNS Traffic", "Command and Control", "Data Protection", "DNS Hijacking", "Prohibited Traffic Allowed or Protocol Mismatch"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detect hosts connecting to dynamic domain providers action.notable = 1 @@ -3964,7 +3944,7 @@ 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 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 @@ -4103,114 +4083,68 @@ 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 Vulnerability Logs For Endpoint] 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 = 2017-08-24 +action.escu.modification_date = 2017-09-10 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.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["AWS User Monitoring"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Spike in AWS API Activity -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.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.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 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 +action.escu.eli5 = none +action.escu.how_to_implement = You need to be ingesting the logs from your vulnerability scanner. +action.escu.data_models = ["Vulnerabilities"] +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 = ["ColdRoot MacOS RAT", "SamSam Ransomware", "Ransomware", "DNS Hijacking"] +action.escu.fields_required = ["dest"] +action.escu.earliest_time_offset = 604800 +action.escu.latest_time_offset = 0 +description = This search will show you any vulnerabilities noted for a specific endpoint for the last week. 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 [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 = | from datamodel Vulnerabilities.Vulnerabilities | search dest={dest} -[ESCU - Web Fraud - Password Sharing Across Accounts - Rule] +[ESCU - Detect USB device insertion - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-07-12 -action.escu.modification_date = 2018-10-08 -action.escu.asset_at_risk = account -action.escu.channel = webfraud -action.escu.confidence = medium -action.escu.eli5 = A common password across user accounts generally indicates that the users are choosing poor passwords or that a fraudster has a common password across multiple accounts embedded within a script. The search will extract the username and password information from the form_data field, then calculate the number and values for usernames that have the same passwords. Finally, it outputs the values where the unique usernames sharing passwords are greater than 5 -action.escu.how_to_implement = We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream. -action.escu.full_search_name = ESCU - Web Fraud - Password Sharing Across Accounts - Rule -action.escu.mappings = {"cis20": ["CIS 16"], "nist": ["DE.DP"]} -action.escu.known_false_positives = As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior. +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 = 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 = ["Splunk Stream", "Palo Alto Firewall", "Bro"] -action.escu.analytic_story = ["Web Fraud Detection"] +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Data Protection"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Web Fraud - Password Sharing Across Accounts +action.correlationsearch.label = Detect USB device insertion action.notable = 1 -action.notable.param.nes_fields = user -action.notable.param.rule_description = This search is used to identify user accounts, $user$, that share common passwords -action.notable.param.rule_title = Web Fraud Detection: Password Sharing Across Accounts -action.notable.param.security_domain = threat -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 - Get Emails From Specific Sender\n - ESCU - Get Web Session Information via session_id\n"} +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 = 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 = user -action.risk.param._risk_object_type = other -action.risk.param._risk_score = 10 +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 = user -alert.suppress.period = 3600s +alert.suppress.fields = dest +alert.suppress.period = 86400s cron_schedule = 0 * * * * -description = This search is used to identify user accounts that share a common password. +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 @@ -4221,7 +4155,7 @@ quantity = 0 realtime_schedule = 0 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 +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 - Shim Database Installation With Suspicious Parameters - Rule] action.escu = 0 @@ -4272,45 +4206,44 @@ 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)` -[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 = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] 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 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 = 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 @@ -4320,7 +4253,7 @@ 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 - Detect Excessive User Account Lockouts - Rule] action.escu = 0 @@ -4371,29 +4304,28 @@ schedule_window = auto is_visible = false search = sourcetype=WinEventLog:Security EventCode=4740 | stats count min(_time) as firstTime max(_time) as lastTime by user, signature | `ctime(firstTime)` | `ctime(lastTime)` | search count > 5 -[ESCU - Get Vulnerability Logs For Endpoint] +[ESCU - Get All AWS Activity From City] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-08-24 -action.escu.modification_date = 2017-09-10 +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 need to be ingesting the logs from your vulnerability scanner. -action.escu.data_models = ["Vulnerabilities"] -action.escu.full_search_name = ESCU - Get Vulnerability Logs For Endpoint +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 = contextual -action.escu.providing_technologies = ["Nessus"] -action.escu.analytic_story = ["SamSam Ransomware", "Ransomware", "DNS Hijacking", "ColdRoot MacOS RAT"] -action.escu.fields_required = ["dest"] -action.escu.earliest_time_offset = 604800 +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 will show you any vulnerabilities noted for a specific endpoint for the last week. +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 = | from datamodel Vulnerabilities.Vulnerabilities | search dest={dest} +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 - RunDLL Loading DLL By Ordinal - Rule] action.escu = 0 @@ -4494,6 +4426,73 @@ 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 - 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 = ["AWS Network ACL Activity", "Command and Control", "Suspicious AWS Traffic"] +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 Interface details via resourceId\n - ESCU - Get Notable History\n - ESCU - Get Notable Info\n\n2. [[action|escu_investigate]]: Based on ESCU investigate recommendations:\n - ESCU - Get All AWS Activity From IP Address\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 - Identify New User Accounts - Rule] action.escu = 0 action.escu.enabled = 1 @@ -4544,6 +4543,55 @@ 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.search_type = detection +action.escu.providing_technologies = ["AWS"] +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: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 - Detect Large Outbound ICMP Packets - Rule] action.escu = 0 action.escu.enabled = 1 @@ -4594,55 +4642,6 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest values(All_Traffic.action) values(All_Traffic.bytes) from datamodel=Network_Traffic where All_Traffic.action !=blocked All_Traffic.dest_category !=internal (All_Traffic.protocol=icmp OR All_Traffic.transport=icmp) All_Traffic.bytes > 1000 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 - First time seen command line argument - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-04-09 -action.escu.modification_date = 2018-04-16 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = The subsearch returns all events where `cmd.exe` was used with a `/c` parameter in the command-line arguments to execute other commands/programs. It appends the historical data to those results in the lookup file. Next, it recalculates the `firstTime` and `lastTime` field for command-line execution and outputs this data to the lookup file to update the local cache. It returns only those events that have first been seen in the past four hours. This is combined with the main search to return the time, user, destination, process, parent process, and value of the command-line argument. -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 Technology Add-on (TA). Please make sure you run the support search "Previously seen command line arguments,"—which creates a lookup file called `previously_seen_cmd_line_arguments.csv`—a historical baseline of all command-line arguments. You must also validate this list. -action.escu.full_search_name = ESCU - First time seen command line argument - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Scripting", "Persistence", "Command-Line Interface"], "kill_chain_phases": ["Command and Control", "Actions on Objectives"], "cis20": ["CIS 3", "CIS 8"], "nist": ["PR.PT", "DE.CM", "PR.IP"]} -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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Hidden Cobra Malware", "Orangeworm Attack Group", "DHS Report TA18-074A"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = First time seen command line argument -action.notable = 1 -action.notable.param.nes_fields = dest, user, process, cmdline -action.notable.param.rule_description = The system $dest$ executed a command-line argument, $cmdline$, that has not previously been seen. -action.notable.param.rule_title = First-time seen command-line argument was 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.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, cmdline -alert.suppress.period = 86400s -cron_schedule = 30 * * * * -description = This search looks for command-line arguments that use a `/c` parameter to execute a command that has not previously been seen. -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 process=cmd.exe cmdline="* /c *" [ search sourcetype=XmlWinEventLog:Microsoft-Windows-Sysmon/Operational process=cmd.exe cmdline="* /c *" | stats earliest(_time) as firstTime latest(_time) as lastTime by cmdline | inputlookup append=t previously_seen_cmd_line_arguments | stats min(firstTime) as firstTime, max(lastTime) as lastTime by cmdline | outputlookup previously_seen_cmd_line_arguments | eval newCmdLineArgument=if(firstTime >= relative_time(now(), "-70m@m"), 1, 0) | where newCmdLineArgument=1 | `ctime(firstTime)` | `ctime(lastTime)` | table cmdline] | table _time, user,dest, process, parent_process, cmdline - [ESCU - Get Logon Rights Modifications For User] action.escu = 0 action.escu.enabled = 1 @@ -4666,54 +4665,28 @@ 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 - Identify Systems Using Remote Desktop] 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 = 2017-04-18 +action.escu.modification_date = 2017-09-15 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 +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 -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 +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 - Scheduled Task Name Used by Dragonfly Threat Actors - Rule] action.escu = 0 @@ -4778,7 +4751,7 @@ 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", "DNS Hijacking", "Dynamic DNS", "ColdRoot MacOS RAT"] +action.escu.analytic_story = ["Splunk Enterprise Vulnerability CVE-2018-11409", "Dynamic DNS", "ColdRoot MacOS RAT", "DNS Hijacking"] action.escu.fields_required = ["src_ip"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 3600 @@ -4802,7 +4775,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Splunk Enterprise Vulnerability", "Credential Dumping", "Suspicious MSHTA Activity", "DNS Amplification Attacks", "Monitor for Updates", "SamSam Ransomware", "Malicious PowerShell", "Asset Tracking", "Prohibited Traffic Allowed or Protocol Mismatch", "Emotet Malware (TA18-201A)", "Use of Cleartext Protocols", "Apache Struts Vulnerability", "Windows Persistence Techniques", "Account Monitoring and Controls", "Hidden Cobra Malware", "Ransomware", "Suspicious Windows Registry Activities", "Suspicious Emails", "DNS Hijacking", "Windows Log Manipulation", "Lateral Movement", "Windows Service Abuse", "Netsh Abuse", "Dynamic DNS", "Host Redirection", "Monitor Backup Solution", "Spectre And Meltdown Vulnerabilities", "Command and Control", "JBoss Vulnerability", "Monitor for Unauthorized Software", "Brand Monitoring", "Suspicious WMI Use", "Router & Infrastructure Security", "Orangeworm Attack Group", "Suspicious DNS Traffic", "Windows Privilege Escalation", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "Unusual Processes", "SQL Injection", "ColdRoot MacOS RAT", "Collection and Staging", "Disabling Security Tools", "Data Protection"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Splunk Enterprise Vulnerability", "Credential Dumping", "SQL Injection", "Suspicious WMI Use", "Lateral Movement", "Disabling Security Tools", "Netsh Abuse", "Emotet Malware (TA18-201A)", "Account Monitoring and Controls", "Windows Persistence Techniques", "Orangeworm Attack Group", "JBoss Vulnerability", "Dynamic DNS", "Unusual Processes", "Brand Monitoring", "Suspicious MSHTA Activity", "Windows Log Manipulation", "Windows Privilege Escalation", "Asset Tracking", "ColdRoot MacOS RAT", "Suspicious DNS Traffic", "Command and Control", "SamSam Ransomware", "DNS Amplification Attacks", "Router & Infrastructure Security", "Monitor for Unauthorized Software", "Collection and Staging", "Windows Service Abuse", "Suspicious Windows Registry Activities", "Host Redirection", "Suspicious Emails", "Ransomware", "Hidden Cobra Malware", "Spectre And Meltdown Vulnerabilities", "Apache Struts Vulnerability", "Monitor for Updates", "Windows File Extension and Association Abuse", "Malicious PowerShell", "Data Protection", "Monitor Backup Solution", "DHS Report TA18-074A", "Use of Cleartext Protocols", "DNS Hijacking", "Prohibited Traffic Allowed or Protocol Mismatch"] action.escu.fields_required = ["user"] action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0 @@ -4826,7 +4799,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Credential Dumping", "Suspicious MSHTA Activity", "SamSam Ransomware", "Malicious PowerShell", "Emotet Malware (TA18-201A)", "Windows Persistence Techniques", "Hidden Cobra Malware", "Ransomware", "Suspicious Windows Registry Activities", "Windows Log Manipulation", "Lateral Movement", "Windows Service Abuse", "Netsh Abuse", "Windows Defense Evasion Tactics", "Command and Control", "Monitor for Unauthorized Software", "Suspicious WMI Use", "Orangeworm Attack Group", "Windows Privilege Escalation", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "Unusual Processes", "Collection and Staging", "Disabling Security Tools"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Credential Dumping", "Suspicious WMI Use", "Lateral Movement", "Disabling Security Tools", "Netsh Abuse", "Emotet Malware (TA18-201A)", "Windows Persistence Techniques", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "Windows Defense Evasion Tactics", "Unusual Processes", "Suspicious MSHTA Activity", "Windows Log Manipulation", "Windows Privilege Escalation", "Command and Control", "SamSam Ransomware", "Monitor for Unauthorized Software", "Collection and Staging", "Windows Service Abuse", "Suspicious Windows Registry Activities", "Ransomware", "Hidden Cobra Malware", "Windows File Extension and Association Abuse", "Malicious PowerShell", "DHS Report TA18-074A"] action.escu.fields_required = ["process", "dest"] action.escu.earliest_time_offset = 7200 action.escu.latest_time_offset = 7200 @@ -5148,7 +5121,7 @@ action.escu.mappings = {"mitre_attack": ["Privilege Escalation", "Persistence", action.escu.known_false_positives = There are many legitimate applications that must execute upon 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", "Windows Privilege Escalation"] +action.escu.analytic_story = ["Windows Privilege Escalation", "Suspicious Windows Registry Activities"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Registry Keys Used For Privilege Escalation action.notable = 1 @@ -5157,7 +5130,7 @@ action.notable.param.rule_description = A registry key used for privilege escala action.notable.param.rule_title = Registry Key Associated With Privilege Escalation Modified 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 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.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -5182,77 +5155,27 @@ 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] +[ESCU - Baseline of Network ACL Activity by ARN] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-19 -action.escu.modification_date = 2018-03-19 +action.escu.creation_date = 2018-05-21 +action.escu.modification_date = 2018-05-21 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.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 = investigative +action.escu.search_type = support 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 -action.escu.creation_date = 2018-04-09 -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 = The subsearch returns the ARNs of all successful EC2 instance modifications within the last hour and then appends the historical data in the lookup file to those results. EC2 modification APIs are defined by the macro `ec2ModificationAPIs`. The search then recalculates the `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have 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 Launches By User" support search once to create a history of previously seen ARNs. To add or remove APIs that modify an EC2 instance, edit the macro `ec2ModificationAPIs`. -action.escu.full_search_name = ESCU - EC2 Instance Modified With Previously Unseen User - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.known_false_positives = It's possible that a new user will start to modify EC2 instances when they haven't before for any number of reasons. Verify with the user that is modifying instances that this is the intended behavior. -action.escu.search_type = detection -action.escu.providing_technologies = ["AWS"] -action.escu.analytic_story = ["Unusual AWS EC2 Modifications"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Modified With Previously Unseen User -action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = The EC2 instance $dest$ was modified by $user$. This user has never modified an EC2 instance before. -action.notable.param.rule_title = EC2 Instance Modified By Previously Unseen User $user$ -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 Instance Details by instanceId\n - ESCU - Get Notable History\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 = 30 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = user, dest -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for EC2 instances being modified by users who have not previously modified them. -dispatch.earliest_time = -70m@m +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=aws:cloudtrail `ec2ModificationAPIs` [search sourcetype=aws:cloudtrail `ec2ModificationAPIs` 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_modifications_by_user | stats min(firstTime) as firstTime, max(lastTime) as lastTime by arn | outputlookup previously_seen_ec2_modifications_by_user | 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] | spath output=dest responseElements.instancesSet.items{}.instanceId | spath output=user userIdentity.arn | table _time, user, dest +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 - AWS S3 Bucket details via bucketName] action.escu = 0 @@ -5428,55 +5351,28 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) | where isOutlier=1| `ctime(earliest)`| `ctime(latest)` | `drop_dm_object_name("Authentication")` -[ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule] +[ESCU - Discover DNS records] 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 = 2019-02-14 +action.escu.modification_date = 2019-02-14 action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = This search returns the number of times a URL associated with this type of JexBoss probe is observed. -action.escu.how_to_implement = You must be ingesting data from the web server or network traffic that contains web specific information, and populating the Web data model. -action.escu.data_models = ["Web"] -action.escu.full_search_name = ESCU - Detect attackers scanning for vulnerable JBoss servers - Rule -action.escu.mappings = {"mitre_attack": ["Discovery", "System Information Discovery"], "kill_chain_phases": ["Reconnaissance"]} -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.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect attackers scanning for vulnerable JBoss servers -action.notable = 1 -action.notable.param.nes_fields = -action.notable.param.rule_description = This search looks for specific GET/HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. -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.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,url -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = This search looks for specific GET or HEAD requests to web servers that are indicative of reconnaissance attempts to identify vulnerable JBoss servers. JexBoss is described as the exploit tool of choice for this malicious activity. -dispatch.earliest_time = -70m@m +action.escu.eli5 = Discover the DNS records and their answers for domains owned by the company using network traffic events. The discovered events are exported as a lookup named `discovered_dns_records.csv` +action.escu.how_to_implement = To successfully implement this search, you must be ingesting network traffic, and populating the Network_Traffic data model. Also make sure that the cim_corporate_web_domains and cim_corporate_email_domains lookups are populated with the domains owned by your corporation +action.escu.data_models = ["Network_Resolution"] +action.escu.full_search_name = ESCU - Discover DNS records +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 = ["DNS Hijacking"] +description = The search takes corporate and common cloud provider domains configured under cim_corporate_email_domains.csv, cim_corporate_web_domains.csv, and cloud_domains.csv and finds their responses across the last 30 days from data in the Network Traffic datamodel, then stores the output under the discovered_dns_records.csv lookup +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=Web where (Web.http_method="GET" OR Web.http_method="HEAD") AND (Web.url="*/web-console/ServerInfo.jsp*" OR Web.url="*web-console*" OR Web.url="*jmx-console*" OR Web.url = "*invoker*") by Web.http_method, Web.url, Web.src, Web.dest | `drop_dm_object_name("Web")` | `ctime(firstTime)` | `ctime(lastTime)` +search = | inputlookup cim_corporate_email_domains.csv | inputlookup append=T cim_corporate_web_domains.csv | inputlookup append=T cim_cloud_domains.csv | eval domain = trim(replace(domain, "\*", "")) | join domain [|tstats summariesonly=true count values(DNS.record_type) as type, values(DNS.answer) as answer from datamodel=Network_Resolution where DNS.message_type=RESPONSE DNS.answer!="unknown" DNS.answer!="" by DNS.query | rename DNS.query as query | where query!="unknown" | rex field=query "(?\w+\.\w+?)(?:$|/)"] | makemv delim=" " answer | makemv delim=" " type | sort -count | table count,domain,type,query,answer | outputlookup createinapp=true discovered_dns_records.csv [ESCU - Investigate Successful Remote Desktop Authentications] action.escu = 0 @@ -5502,128 +5398,6 @@ 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] -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 = ["SamSam Ransomware", "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 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 - EC2 Instance Started With Previously Unseen User - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-03-15 -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 ARNs 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 `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have 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 Launches By User" support search once to create a history of previously seen ARNs. -action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen User - Rule -action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} -action.escu.known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. -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 = EC2 Instance Started With Previously Unseen User -action.notable = 1 -action.notable.param.nes_fields = user, dest -action.notable.param.rule_description = The EC2 instance $dest$ was created by $user$. This user has never created an EC2 instance before. -action.notable.param.rule_title = EC2 Instance Created By Previously Unseen User $user$ -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 = user, dest -alert.suppress.period = 14400s -cron_schedule = 0 * * * * -description = This search looks for EC2 instances being created by users who have not created them before. -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 | 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 - Detect new user AWS Console Login - Rule] action.escu = 0 action.escu.enabled = 1 @@ -5673,6 +5447,55 @@ 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" +[ESCU - EC2 Instance Started With Previously Unseen User - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-03-15 +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 ARNs 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 `firstTime` and `lastTime` field for each ARN and returns only those ARNs that have 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 Launches By User" support search once to create a history of previously seen ARNs. +action.escu.full_search_name = ESCU - EC2 Instance Started With Previously Unseen User - Rule +action.escu.mappings = {"cis20": ["CIS 1"], "nist": ["ID.AM"]} +action.escu.known_false_positives = It's possible that a user will start to create EC2 instances when they haven't before for any number of reasons. Verify with the user that is launching instances that this is the intended behavior. +action.escu.search_type = detection +action.escu.providing_technologies = ["AWS"] +action.escu.analytic_story = ["AWS Cryptomining", "Suspicious AWS EC2 Activities"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = EC2 Instance Started With Previously Unseen User +action.notable = 1 +action.notable.param.nes_fields = user, dest +action.notable.param.rule_description = The EC2 instance $dest$ was created by $user$. This user has never created an EC2 instance before. +action.notable.param.rule_title = EC2 Instance Created By Previously Unseen User $user$ +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 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 = 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 = user, dest +alert.suppress.period = 14400s +cron_schedule = 0 * * * * +description = This search looks for EC2 instances being created by users who have not created them before. +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 | 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 - Previously seen command line arguments] action.escu = 0 action.escu.enabled = 1 @@ -5685,7 +5508,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Hidden Cobra Malware", "Orangeworm Attack Group", "DHS Report TA18-074A"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "Hidden Cobra Malware", "DHS Report TA18-074A"] 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 @@ -5794,43 +5617,44 @@ 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 - Detect Mimikatz Via PowerShell And EventCode 4663 - Rule] +[ESCU - DNS Query Requests Resolved by Unauthorized DNS Servers - 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 = 2017-07-08 +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 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 = 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 = ["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", "DNS Hijacking"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect Mimikatz Via PowerShell And EventCode 4663 +action.correlationsearch.label = DNS Query Requests Resolved by Unauthorized DNS Servers 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 = 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.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 - 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 - ESCU - Get Vulnerability Logs For Endpoint\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 History Of Email Sources\n - ESCU - Get Process responsible for the DNS traffic\n - ESCU - Investigate Web Activity From src_ip\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.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = user, dest, process -alert.suppress.period = 86400s +alert.suppress.fields = dest,src +alert.suppress.period = 28800s cron_schedule = 0 * * * * -description = This search looks for PowerShell reading lsass memory consistent with credential dumping. +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 @@ -5841,7 +5665,7 @@ 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 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 - Get All AWS Activity From Country] action.escu = 0 @@ -6060,28 +5884,6 @@ 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 - Baseline of Network ACL Activity by ARN] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2018-05-21 -action.escu.modification_date = 2018-05-21 -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.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 -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 - [ESCU - Get Authentication Logs For Endpoint] action.escu = 0 action.escu.enabled = 1 @@ -6095,7 +5897,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Splunk Enterprise Vulnerability", "Credential Dumping", "Suspicious MSHTA Activity", "Monitor for Updates", "SamSam Ransomware", "Malicious PowerShell", "Asset Tracking", "Prohibited Traffic Allowed or Protocol Mismatch", "Emotet Malware (TA18-201A)", "Apache Struts Vulnerability", "Windows Persistence Techniques", "Account Monitoring and Controls", "Hidden Cobra Malware", "Ransomware", "Suspicious Windows Registry Activities", "Suspicious Emails", "DNS Hijacking", "Windows Log Manipulation", "Lateral Movement", "Windows Service Abuse", "Netsh Abuse", "Windows Defense Evasion Tactics", "Dynamic DNS", "Host Redirection", "Spectre And Meltdown Vulnerabilities", "Command and Control", "JBoss Vulnerability", "Monitor for Unauthorized Software", "Brand Monitoring", "Suspicious WMI Use", "Router & Infrastructure Security", "Orangeworm Attack Group", "Suspicious DNS Traffic", "Windows Privilege Escalation", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "Unusual Processes", "SQL Injection", "ColdRoot MacOS RAT", "Collection and Staging", "Disabling Security Tools", "Data Protection"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Splunk Enterprise Vulnerability", "Credential Dumping", "SQL Injection", "Suspicious WMI Use", "Lateral Movement", "Disabling Security Tools", "Netsh Abuse", "Emotet Malware (TA18-201A)", "Account Monitoring and Controls", "Windows Persistence Techniques", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "JBoss Vulnerability", "Windows Defense Evasion Tactics", "Dynamic DNS", "Unusual Processes", "Brand Monitoring", "Suspicious MSHTA Activity", "Windows Log Manipulation", "Windows Privilege Escalation", "Asset Tracking", "ColdRoot MacOS RAT", "Suspicious DNS Traffic", "Command and Control", "SamSam Ransomware", "Router & Infrastructure Security", "Monitor for Unauthorized Software", "Collection and Staging", "Windows Service Abuse", "Suspicious Windows Registry Activities", "Host Redirection", "Suspicious Emails", "Ransomware", "Hidden Cobra Malware", "Spectre And Meltdown Vulnerabilities", "Apache Struts Vulnerability", "Monitor for Updates", "Windows File Extension and Association Abuse", "Malicious PowerShell", "Data Protection", "DHS Report TA18-074A", "DNS Hijacking", "Prohibited Traffic Allowed or Protocol Mismatch"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 43200 action.escu.latest_time_offset = 1 @@ -6170,7 +5972,7 @@ action.escu.mappings = {"mitre_attack": ["Defense Evasion", "Indicator Removal o action.escu.known_false_positives = The wevtutil.exe application is a legitimate Windows event log utility. Administrators may use it to manage Windows event logs. action.escu.search_type = detection action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Ransomware", "Windows Log Manipulation"] +action.escu.analytic_story = ["Windows Log Manipulation", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Suspicious wevtutil Usage action.notable = 1 @@ -6179,7 +5981,7 @@ action.notable.param.rule_description = wevtutil is the windows event log tool. action.notable.param.rule_title = Suspicious wevtutil Usage 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 Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -6288,7 +6090,7 @@ 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", "Command and Control", "Suspicious AWS Traffic"] action.escu.fields_required = ["resourceId"] action.escu.earliest_time_offset = 86400 action.escu.latest_time_offset = 0 @@ -6299,93 +6101,65 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:config resourceId={resourceId} | table _time ARN relationships{}.resourceType relationships{}.name relationships{}.resourceId configuration.privateIpAddresses{}.privateIpAddress configuration.privateIpAddresses{}.association.publicIp -[ESCU - Monitor Web Traffic For Brand Abuse - Rule] -action.escu = 0 -action.escu.enabled = 1 -action.escu.creation_date = 2017-06-01 -action.escu.modification_date = 2017-09-23 -action.escu.asset_at_risk = Endpoint -action.escu.channel = ESCU -action.escu.confidence = high -action.escu.eli5 = This search looks at all the URLs an endpoint is connecting to and then checks the URL against a list of faux domains that could be indicative of brand abuse. -action.escu.how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. -action.escu.data_models = ["Web"] -action.escu.full_search_name = ESCU - Monitor Web Traffic For Brand Abuse - 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 = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] -action.escu.analytic_story = ["Brand Monitoring"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Monitor Web Traffic For Brand Abuse -action.notable = 1 -action.notable.param.nes_fields = src, url -action.notable.param.rule_description = The host $src$ connected to a web site with a domain similar to that which you are monitoring for brand abuse. -action.notable.param.rule_title = Web URL Brand Abuse from $src$ -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 - ESCU - Get DNS Server History for a host\n - ESCU - Get Email Info\n - ESCU - Get Emails From Specific Sender\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 = src -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 = src -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. -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` 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 - EC2 Instance Started With Previously Unseen Instance Type - Rule] +[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 = 2018-03-12 +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 = 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.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 = ["AWS"] -action.escu.analytic_story = ["AWS Cryptomining"] +action.escu.analytic_story = ["AWS User Monitoring"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = EC2 Instance Started With Previously Unseen Instance Type +action.correlationsearch.label = Detect Spike in AWS API Activity 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.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 EC2 Launch Details\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 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_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 +alert.suppress.fields = user alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = This search looks for EC2 instances being created with previously unseen instance types. +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 @@ -6396,7 +6170,101 @@ 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 = 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 PsExec With accepteula Flag - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-03-28 +action.escu.modification_date = 2018-03-28 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = In this search, we are looking for the PsExec process with `accepteula` on the command line. +action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, command-line arguments, and parent process from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon Technology Add-on (TA). +action.escu.full_search_name = ESCU - Detect PsExec With accepteula Flag - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} +action.escu.known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine +action.escu.search_type = detection +action.escu.providing_technologies = ["Sysmon"] +action.escu.analytic_story = ["SamSam Ransomware", "DHS Report TA18-074A"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Detect PsExec With accepteula Flag +action.notable = 1 +action.notable.param.nes_fields = dest,parent_process +action.notable.param.rule_description = The process pssxec.exe was run with the -accepteula flag on $dest$ by $user$. +action.notable.param.rule_title = PsExec executed with accepteula flag 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 = 75 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest, parent_process +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within 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=xmlwineventlog:microsoft-windows-sysmon/operational process=PsExec.exe accepteula | search cmdline=*accepteula* | stats count values(cmdline) as cmdlines, min(_time) as firstTime, max(_time) as lastTime by dest, user, parent_process | `ctime(firstTime)`| `ctime(lastTime)` | table firstTime, lastTime, count, dest, user, parent_process, cmdlines + +[ESCU - Identify Systems Receiving Remote Desktop Traffic] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-04-24 +action.escu.modification_date = 2017-09-15 +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.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 +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 + +[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 = ["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 = 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 @@ -6413,7 +6281,7 @@ 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 = ["Windows Persistence Techniques", "Windows Service Abuse", "Orangeworm Attack Group", "DHS Report TA18-074A", "Disabling Security Tools"] +action.escu.analytic_story = ["Disabling Security Tools", "Windows Persistence Techniques", "Orangeworm Attack Group", "Windows Service Abuse", "DHS Report TA18-074A"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Sc.exe Manipulating Windows Services action.notable = 1 @@ -6447,56 +6315,6 @@ 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 - DNS Query Requests Resolved by Unauthorized DNS Servers - Rule] -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.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.search_type = detection -action.escu.providing_technologies = ["Splunk Stream", "Bro"] -action.escu.analytic_story = ["DNS Hijacking", "Command and Control", "Suspicious DNS Traffic"] -action.correlationsearch.enabled = 1 -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 = 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 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 = 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 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 -enableSched = 1 -counttype = number of events -relation = greater than -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")` - [ESCU - Detect Spike in S3 Bucket deletion - Rule] action.escu = 0 action.escu.enabled = 1 @@ -6584,7 +6402,7 @@ 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 = ["SamSam Ransomware", "Ransomware", "Windows Log Manipulation"] +action.escu.analytic_story = ["Windows Log Manipulation", "SamSam Ransomware", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Deleting Shadow Copies action.notable = 1 @@ -6593,7 +6411,7 @@ 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 Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -6653,7 +6471,7 @@ 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 EC2 Activities", "Unusual AWS EC2 Modifications", "Suspicious AWS S3 Activities", "Suspicious AWS Login Activities", "AWS Network ACL Activity"] +action.escu.analytic_story = ["AWS Network ACL Activity", "Suspicious AWS S3 Activities", "Suspicious AWS EC2 Activities", "Suspicious AWS Login Activities", "Unusual AWS EC2 Modifications"] action.escu.fields_required = ["arn"] action.escu.earliest_time_offset = 14400 action.escu.latest_time_offset = 0 @@ -6664,45 +6482,46 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:cloudtrail userIdentity.arn={arn} | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType -[ESCU - Schtasks used for forcing a reboot - Rule] +[ESCU - Large Volume of DNS ANY Queries - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2017-11-03 -action.escu.modification_date = 2017-11-03 -action.escu.asset_at_risk = Endpoint +action.escu.creation_date = 2016-08-24 +action.escu.modification_date = 2017-09-20 +action.escu.asset_at_risk = DNS Servers 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 that would cause a forced reboot on the 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 establish persistence. This tactic is leveraged by the Bad Rabbit Ransomware. -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 used for forcing a reboot - Rule -action.escu.mappings = {"mitre_attack": ["Persistence", "Execution", "Scheduled Task"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3"], "nist": ["PR.IP"]} -action.escu.known_false_positives = Administrators may create jobs on systems forcing reboots to perform updates, maintenance, etc. +action.escu.confidence = high +action.escu.eli5 = This search counts the number of DNS ANY queries received in 5 minutes, and generates a Notable Event if the count exceeds a predefined threshold. The search returns the count, the first time, and the last time a DNS packet was observed with the ANY flag set. +action.escu.how_to_implement = To successfully implement this search you must ensure that DNS data is populating the Network_Resolution data model. +action.escu.data_models = ["Network_Resolution"] +action.escu.full_search_name = ESCU - Large Volume of DNS ANY Queries - Rule +action.escu.mappings = {"kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 11", "CIS 12"], "nist": ["PR.PT", "DE.AE", "PR.IP"]} +action.escu.known_false_positives = Legitimate ANY requests may trigger this search, however it is unusual to see a large volume of them under typical circumstances. You may modify the threshold in the search to better suit your environment. action.escu.search_type = detection -action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] -action.escu.analytic_story = ["Windows Persistence Techniques", "Ransomware"] +action.escu.providing_technologies = ["Splunk Stream", "Bro"] +action.escu.analytic_story = ["DNS Amplification Attacks"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Schtasks used for forcing a reboot +action.correlationsearch.label = Large Volume of DNS ANY Queries 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 scheduled to force a reboot -action.notable.param.rule_title = Schtasks used for scheduling a force reboot -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 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.nes_fields = dest +action.notable.param.rule_description = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. +action.notable.param.rule_title = Large Volume of DNS ANY Queries +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 Notable Info\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"} 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_score = 60 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 that a forced reboot of system is scheduled. -dispatch.earliest_time = -5h@h -dispatch.latest_time = -1h@h +alert.suppress.fields = dest +alert.suppress.period = 7200s +cron_schedule = */5 * * * * +description = The search is used to identify attempts to use your DNS Infrastructure for DDoS purposes via a DNS amplification attack leveraging ANY queries. +dispatch.earliest_time = -15m@m +dispatch.latest_time = -10m@m disabled=true enableSched = 1 counttype = number of events @@ -6711,7 +6530,7 @@ quantity = 0 realtime_schedule = 0 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)` +search = | tstats `summariesonly` count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" "DNS.record_type"="ANY" by "DNS.dest" | `drop_dm_object_name("DNS")` | where count>200 [ESCU - Count of assets by category] action.escu = 0 @@ -6749,7 +6568,7 @@ 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 = ["Suspicious Emails", "Brand Monitoring"] +action.escu.analytic_story = ["Brand Monitoring", "Suspicious Emails"] action.escu.fields_required = ["message_id"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 7200 @@ -6804,6 +6623,105 @@ 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 = ["SamSam Ransomware", "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 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 @@ -6876,54 +6794,28 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:description id={networkAclId} | table id account_id vpc_id network_acl_entries{}.* -[ESCU - Detect PsExec With accepteula Flag - Rule] +[ESCU - Get EC2 Instance Details by instanceId] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-03-28 -action.escu.modification_date = 2018-03-28 -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 = In this search, we are looking for the PsExec process with `accepteula` on the command line. -action.escu.how_to_implement = To successfully implement this search, you need to be ingesting logs with the process name, command-line arguments, and parent process from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon Technology Add-on (TA). -action.escu.full_search_name = ESCU - Detect PsExec With accepteula Flag - Rule -action.escu.mappings = {"mitre_attack": ["Execution", "Command-Line Interface"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 8"], "nist": ["PR.PT", "DE.CM"]} -action.escu.known_false_positives = Administrators can leverage PsExec for accessing remote systems and might pass `accepteula` as an argument if they are running this tool for the first time. However, it is not likely that you'd see multiple occurrences of this event on a machine -action.escu.search_type = detection -action.escu.providing_technologies = ["Sysmon"] -action.escu.analytic_story = ["SamSam Ransomware", "DHS Report TA18-074A"] -action.correlationsearch.enabled = 1 -action.correlationsearch.label = Detect PsExec With accepteula Flag -action.notable = 1 -action.notable.param.nes_fields = dest,parent_process -action.notable.param.rule_description = The process pssxec.exe was run with the -accepteula flag on $dest$ by $user$. -action.notable.param.rule_title = PsExec executed with accepteula flag 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 = 75 -action.risk.param.verbose = 0 -alert.digest_mode = 1 -alert.suppress = 1 -alert.suppress.fields = dest, parent_process -alert.suppress.period = 86400s -cron_schedule = 0 * * * * -description = This search looks for events where `PsExec.exe` is run with the `accepteula` flag in the command line. PsExec is a built-in Windows utility that enables you to execute processes on other systems. It is fully interactive for console applications. This tool is widely used for launching interactive command prompts on remote systems. Threat actors leverage this extensively for executing code on compromised systems. If an attacker is running PsExec for the first time, they will be prompted to accept the end-user license agreement (EULA), which can be passed as the argument `accepteula` within 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 = ["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 -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=PsExec.exe accepteula | search cmdline=*accepteula* | stats count values(cmdline) as cmdlines, min(_time) as firstTime, max(_time) as lastTime by dest, user, parent_process | `ctime(firstTime)`| `ctime(lastTime)` | table firstTime, lastTime, count, dest, user, parent_process, cmdlines +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 - Reg.exe Manipulating Windows Services Registry Keys - Rule] action.escu = 0 @@ -7187,7 +7079,7 @@ 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 = ["Suspicious Command-Line Executions", "Credential Dumping", "SamSam Ransomware", "Emotet Malware (TA18-201A)", "Ransomware", "Suspicious Emails", "Netsh Abuse", "Host Redirection", "Monitor for Unauthorized Software", "Brand Monitoring", "Orangeworm Attack Group", "Unusual Processes"] +action.escu.analytic_story = ["Credential Dumping", "Netsh Abuse", "Emotet Malware (TA18-201A)", "Orangeworm Attack Group", "Suspicious Command-Line Executions", "Unusual Processes", "Brand Monitoring", "SamSam Ransomware", "Monitor for Unauthorized Software", "Host Redirection", "Suspicious Emails", "Ransomware"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 3600 action.escu.latest_time_offset = 3600 @@ -7198,67 +7090,44 @@ schedule_window = auto is_visible = false search = | from datamodel Web.Web | search src={dest} -[ESCU - Get All AWS Activity From City] +[ESCU - Execution of File with Multiple Extensions - Rule] 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.creation_date = 2018-01-26 +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 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.confidence = high +action.escu.eli5 = This search uses the "Application State" data model to look for process names with specific combinations of double extensions. Relatively straightforward, the search looks for strings in the "process" field that match what you're looking for. +action.escu.how_to_implement = To successfully implement this search, you must be ingesting data that records process activity from your hosts to populate the endpoint data model in the processes node. 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.full_search_name = ESCU - Execution of File with Multiple Extensions - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Persistence", "Change Default File Association"], "kill_chain_phases": ["Actions on Objectives"], "cis20": ["CIS 3", "CIS 8"], "nist": ["DE.CM", "PR.PT", "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 = ["Malicious PowerShell"] +action.escu.analytic_story = ["Windows File Extension and Association Abuse"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Malicious PowerShell Process - Encoded Command +action.correlationsearch.label = Execution of File with Multiple Extensions 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.nes_fields = dest, process +action.notable.param.rule_description = The system $dest$ executed a file with a double extension. +action.notable.param.rule_title = Process With Multiple Extensions Launched 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.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 = dest action.risk.param._risk_object_type = system -action.risk.param._risk_score = 20 +action.risk.param._risk_score = 60 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = dest, user, process_name -alert.suppress.period = 14400s +alert.suppress.fields = dest, process +alert.suppress.period = 28800s 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. +description = This search looks for processes launched from files that have double extensions in the file name. This is typically done to obscure the "real" file extension and make it appear as though the file being accessed is a data file, as opposed to executable content. dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -7269,30 +7138,7 @@ 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 +search = | tstats `summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = *.doc.exe OR Processes.process = *.htm.exe OR Processes.process = *.html.exe OR Processes.process = *.txt.exe OR Processes.process = *.pdf.exe OR Processes.process = *.doc.exe by Processes.dest Processes.user Processes.process Processes.parent_process | `ctime(firstTime)` | `ctime(lastTime)` | `drop_dm_object_name(Processes)` [ESCU - Monitor Email For Brand Abuse - Rule] action.escu = 0 @@ -7367,54 +7213,28 @@ 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 - Schtasks scheduling job on remote system - Rule] +[ESCU - Add Prohibited Processes to Enterprise Security] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2016-09-13 +action.escu.creation_date = 2017-06-27 action.escu.modification_date = 2017-09-15 -action.escu.asset_at_risk = Endpoint action.escu.channel = ESCU -action.escu.confidence = medium -action.escu.eli5 = 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 = 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 -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 = | 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 - Single Letter Process On Endpoint - Rule] action.escu = 0 @@ -7565,29 +7385,55 @@ 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 - Get First Occurrence and Last Occurrence of a MAC Address] +[ESCU - Remote Desktop Network Traffic - Rule] 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 = 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 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. +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 = ["Lateral Movement", "SamSam Ransomware", "Hidden Cobra Malware"] +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 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 = 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 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 = | 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 - Monitor DNS For Brand Abuse - Rule] action.escu = 0 @@ -7834,27 +7680,78 @@ schedule_window = auto is_visible = false search = | search sourcetype=aws:cloudtrail user={user} | table _time userIdentity.type userIdentity.userName userIdentity.arn aws_account_id src awsRegion eventName eventType -[ESCU - Baseline of API Calls per User ARN] +[ESCU - Attempted Credential Dump From Registry Via Reg.exe - 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 = 2018-08-28 +action.escu.modification_date = 2018-12-02 +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.confidence = High +action.escu.eli5 = This search looks for the process reg.exe with the "save" parameter, which specifies a binary export from the registry. In addition, it looks for the keys that contain the hashed credentials, which attackers may retrieve and use for brute-force attacks in order to harvest legitimate credentials. +action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. 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 - Attempted Credential Dump From Registry Via Reg.exe - 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 = None identified. +action.escu.search_type = detection +action.escu.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Credential Dumping"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Attempted Credential Dump From Registry Via Reg.exe +action.notable = 1 +action.notable.param.nes_fields = dest, user, process_name +action.notable.param.rule_description = An attempt to save registry keys holding credentials was identified on $dest$. +action.notable.param.rule_title = Attempted Credential Dump From Registry 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 - 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 = process_name, dest +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline, +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=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 - Systems Ready for Spectre-Meltdown Windows Patch] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-01-08 +action.escu.modification_date = 2018-01-08 +action.escu.channel = ESCU +action.escu.eli5 = This search looks to see if a registry key was created at `HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat`. It will tell you when it was created and, if possible, what process created it. +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.data_models = ["Change_Analysis"] +action.escu.full_search_name = ESCU - Systems Ready for Spectre-Meltdown Windows Patch 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.providing_technologies = ["Carbon Black Response", "CrowdStrike Falcon", "Sysmon", "Tanium", "Ziften"] +action.escu.analytic_story = ["Spectre And Meltdown Vulnerabilities"] +description = Some AV applications can cause the Spectre/Meltdown patch for Windows not to install successfully. This registry key is supposed to be created by the AV engine when it has been patched to be able to handle the Windows patch. If this key has been written, the system can then be patched for Spectre and Meltdown. +dispatch.earliest_time = -1d@d dispatch.latest_time = -10m@m disabled=true 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 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="HKLM\Software\Microsoft\Windows\CurrentVersion\QualityCompat*") by All_Changes.dest, All_Changes.command, All_Changes.user, All_Changes.object, All_Changes.object_path | `ctime(lastTime)` | `ctime(firstTime)` | `drop_dm_object_name("All_Changes")` [ESCU - Get EC2 Launch Details] action.escu = 0 @@ -7879,111 +7776,44 @@ 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 - AWS Cross Account Activity From Previously Unseen Account - Rule] +[ESCU - Attempt To Add Certificate To Untrusted Store - 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 - -[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.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 = 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.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 = ["Microsoft Exchange"] -action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "Suspicious Emails"] +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 = Email Attachments With Lots Of Spaces +action.correlationsearch.label = Attempt To Add Certificate To Untrusted Store 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.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 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.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_user -action.risk.param._risk_object_type = user -action.risk.param._risk_score = 60 +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 = src_user +alert.suppress.fields = process, dest 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. +description = Attempt to add a certificate to the untrusted certificate store dispatch.earliest_time = -70m@m dispatch.latest_time = -10m@m disabled=true @@ -7994,7 +7824,52 @@ 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 = | 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 - Get Backup Logs For Endpoint] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-08-24 +action.escu.modification_date = 2017-09-14 +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 = ["SamSam Ransomware", "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 = ["AWS Network ACL Activity", "Command and Control", "Suspicious AWS Traffic"] +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: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 @@ -8103,7 +7978,7 @@ 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 = ["DNS Hijacking", "Dynamic DNS", "Command and Control", "Suspicious DNS Traffic"] +action.escu.analytic_story = ["Dynamic DNS", "Suspicious DNS Traffic", "Command and Control", "DNS Hijacking"] action.escu.fields_required = ["src_ip", "dest_ip"] action.escu.earliest_time_offset = 0 action.escu.latest_time_offset = 86400 @@ -8130,7 +8005,7 @@ 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 = ["SamSam Ransomware", "Emotet Malware (TA18-201A)", "Monitor for Unauthorized Software"] +action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "SamSam Ransomware", "Monitor for Unauthorized Software"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Prohibited Software On Endpoint action.notable = 1 @@ -8214,29 +8089,6 @@ 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 @@ -8422,7 +8274,7 @@ action.escu.mappings = {"mitre_attack": ["Execution", "New Service"], "kill_chai action.escu.known_false_positives = A previously unseen service is not necessarily malicious. Verify that the service is legitimate and that was installed by a legitimate process. action.escu.search_type = detection action.escu.providing_technologies = ["Microsoft Windows"] -action.escu.analytic_story = ["Windows Service Abuse", "Orangeworm Attack Group"] +action.escu.analytic_story = ["Orangeworm Attack Group", "Windows Service Abuse"] action.correlationsearch.enabled = 1 action.correlationsearch.label = First Time Seen Running Windows Service action.notable = 1 @@ -8431,7 +8283,7 @@ action.notable.param.rule_description = The service $serviceName$ is running on action.notable.param.rule_title = First Time Seen Windows Service $serviceName$ 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 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 @@ -8472,7 +8324,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious MSHTA Activity", "Emotet Malware (TA18-201A)", "Windows Persistence Techniques", "Ransomware", "Suspicious Windows Registry Activities", "DHS Report TA18-074A"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Emotet Malware (TA18-201A)", "Windows Persistence Techniques", "Suspicious MSHTA Activity", "Suspicious Windows Registry Activities", "Ransomware", "DHS Report TA18-074A"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Registry Keys Used For Persistence action.notable = 1 @@ -8729,6 +8581,30 @@ 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 First Occurrence and Last Occurrence of a MAC Address] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-06-14 +action.escu.modification_date = 2017-09-13 +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.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 +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)` + [ESCU - Attempt To Stop Security Service - Rule] action.escu = 0 action.escu.enabled = 1 @@ -8778,6 +8654,55 @@ 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 +[ESCU - WMI Permanent Event Subscription - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2018-10-23 +action.escu.modification_date = 2018-10-23 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = medium +action.escu.eli5 = Attackers are increasingly abusing Windows Management Infrastructure (WMI) for stealth, persistence, lateral movement, or just to leverage its functionality. This search looks for the creation of a WMI event subscription by watching for Windows event ID 5861. +action.escu.how_to_implement = To successfully implement this search, you must be ingesting the Windows WMI activity logs. This can be done by adding a stanza to inputs.conf on the system generating logs with a title of [WinEventLog://Microsoft-Windows-WMI-Activity/Operational]. +action.escu.full_search_name = ESCU - WMI Permanent Event Subscription - Rule +action.escu.mappings = {"mitre_attack": ["Execution", "Windows Management Instrumentation", "Persistence", "Windows Management Instrumentation Event Subscription"], "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 = Although unlikely, administrators may use event subscriptions for legitimate purposes. +action.escu.search_type = detection +action.escu.providing_technologies = ["Microsoft Windows"] +action.escu.analytic_story = ["Suspicious WMI Use"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = WMI Permanent Event Subscription +action.notable = 1 +action.notable.param.nes_fields = dest +action.notable.param.rule_description = This search looks for the creation of a permanent WMI event subscription via Windows event logs. +action.notable.param.rule_title = WMI Event Subscription 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 - 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 = 70 +action.risk.param.verbose = 0 +alert.digest_mode = 1 +alert.suppress = 1 +alert.suppress.fields = dest +alert.suppress.period = 28800s +cron_schedule = 0 * * * * +description = This search looks for the creation of WMI permanent event subscriptions. +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:microsoft-windows-wmi-activity/operational" EventCode=5861 Binding | rex field=Message "Consumer =\s+(?[^;|^$]+)" | search consumer!="NTEventLogEventConsumer=\"SCM Event Log Consumer\"" | stats count min(_time) as firstTime max(_time) as lastTime by ComputerName, consumer, Message | `ctime(firstTime)`| `ctime(lastTime)` | rename ComputerName as dest + [ESCU - Previously seen API call per user roles in CloudTrail] action.escu = 0 action.escu.enabled = 1 @@ -8816,7 +8741,7 @@ 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 = ["Windows Privilege Escalation", "Unusual Processes"] +action.escu.analytic_story = ["Unusual Processes", "Windows Privilege Escalation"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Uncommon Processes On Endpoint action.notable = 1 @@ -8825,7 +8750,7 @@ 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 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 @@ -8850,56 +8775,6 @@ 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 - Remote Desktop Network Traffic - Rule] -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.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 = ["SamSam Ransomware", "Hidden Cobra Malware", "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 -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 - Registry Keys for Creating SHIM Databases - Rule] action.escu = 0 action.escu.enabled = 1 @@ -8950,6 +8825,56 @@ 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 - Monitor Web Traffic For Brand Abuse - Rule] +action.escu = 0 +action.escu.enabled = 1 +action.escu.creation_date = 2017-06-01 +action.escu.modification_date = 2017-09-23 +action.escu.asset_at_risk = Endpoint +action.escu.channel = ESCU +action.escu.confidence = high +action.escu.eli5 = This search looks at all the URLs an endpoint is connecting to and then checks the URL against a list of faux domains that could be indicative of brand abuse. +action.escu.how_to_implement = You need to ingest data from your web traffic. This can be accomplished by indexing data from a web proxy, or using a network traffic analysis tool, such as Bro or Splunk Stream. You also need to have run the search "ESCU - DNSTwist Domain Names", which creates the permutations of the domain that will be checked for. +action.escu.data_models = ["Web"] +action.escu.full_search_name = ESCU - Monitor Web Traffic For Brand Abuse - 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 = ["Splunk Stream", "Bro", "Bluecoat", "Palo Alto Firewall"] +action.escu.analytic_story = ["Brand Monitoring"] +action.correlationsearch.enabled = 1 +action.correlationsearch.label = Monitor Web Traffic For Brand Abuse +action.notable = 1 +action.notable.param.nes_fields = src, url +action.notable.param.rule_description = The host $src$ connected to a web site with a domain similar to that which you are monitoring for brand abuse. +action.notable.param.rule_title = Web URL Brand Abuse from $src$ +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 - ESCU - Get DNS Server History for a host\n - ESCU - Get Email Info\n - ESCU - Get Emails From Specific Sender\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 = src +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 = src +alert.suppress.period = 86400s +cron_schedule = 0 * * * * +description = This search looks for Web requests to faux domains similar to the one that you want to have monitored for abuse. +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` 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 - Remote Process Instantiation via WMI - Rule] action.escu = 0 action.escu.enabled = 1 @@ -8965,7 +8890,7 @@ 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 @@ -8974,7 +8899,7 @@ 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 @@ -9071,6 +8996,55 @@ 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)` +[ESCU - Schtasks scheduling job on remote system - Rule] +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.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 +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)` + [ESCU - EC2 Instance Started With Previously Unseen AMI - Rule] action.escu = 0 action.escu.enabled = 1 @@ -9158,7 +9132,7 @@ 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", "Suspicious DNS Traffic", "Data Protection"] +action.escu.analytic_story = ["Suspicious DNS Traffic", "Command and Control", "Data Protection"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Detection of DNS Tunnels action.notable = 1 @@ -9192,44 +9166,44 @@ schedule_window = auto is_visible = false search = | tstats `summariesonly` dc("DNS.query") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.query" | rename "DNS.src" as src "DNS.query" as message | eval length=len(message) | stats sum(length) as length by src | append [ tstats `summariesonly` dc("DNS.answer") as count from datamodel=Network_Resolution where nodename=DNS "DNS.message_type"="QUERY" NOT (`cim_corporate_web_domain_search("DNS.query")`) NOT "DNS.query"="*.in-addr.arpa" NOT ("DNS.src_category"="svc_infra_dns" OR "DNS.src_category"="svc_infra_webproxy" OR "DNS.src_category"="svc_infra_email*" ) by "DNS.src","DNS.answer" | rename "DNS.src" as src "DNS.answer" as message | eval message=if(message=="unknown","", message) | eval length=len(message) | stats sum(length) as length by src ] | stats sum(length) as length by src | where length > 10000 -[ESCU - Attempted Credential Dump From Registry Via Reg.exe - Rule] +[ESCU - Malicious PowerShell Process - Encoded Command - Rule] action.escu = 0 action.escu.enabled = 1 -action.escu.creation_date = 2018-08-28 -action.escu.modification_date = 2018-12-02 +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 = High -action.escu.eli5 = This search looks for the process reg.exe with the "save" parameter, which specifies a binary export from the registry. In addition, it looks for the keys that contain the hashed credentials, which attackers may retrieve and use for brute-force attacks in order to harvest legitimate credentials. -action.escu.how_to_implement = You must be ingesting endpoint data that tracks process activity, including parent-child relationships from your endpoints to populate the Endpoint data model in the Processes node. The command-line arguments are mapped to the "process" field in the Endpoint data model. +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 - Attempted Credential Dump From Registry Via Reg.exe - 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 = None identified. +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 = ["Credential Dumping"] +action.escu.analytic_story = ["Malicious PowerShell"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = Attempted Credential Dump From Registry Via Reg.exe +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 = An attempt to save registry keys holding credentials was identified on $dest$. -action.notable.param.rule_title = Attempted Credential Dump From Registry on $dest$ +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 = 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 - ESCU - Investigate Web Activity From Host\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 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 = 80 +action.risk.param._risk_score = 20 action.risk.param.verbose = 0 alert.digest_mode = 1 alert.suppress = 1 -alert.suppress.fields = process_name, dest -alert.suppress.period = 86400s +alert.suppress.fields = dest, user, process_name +alert.suppress.period = 14400s cron_schedule = 0 * * * * -description = This search looks for execution of reg.exe with parameters specifying an export of keys that contain hashed credentials that attackers may try to crack offline, +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 @@ -9240,7 +9214,7 @@ 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=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*) +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 - Unusually Long Command Line - Rule] action.escu = 0 @@ -9256,7 +9230,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Ransomware", "Unusual Processes"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Suspicious Command-Line Executions", "Unusual Processes", "Ransomware"] action.correlationsearch.enabled = 1 action.correlationsearch.label = Unusually Long Command Line action.notable = 1 @@ -9265,7 +9239,7 @@ action.notable.param.rule_description = An unusually long command-line $cmdline$ action.notable.param.rule_title = Unusually Long Command-Line 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 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 Web Activity From Host\n"} action.notable.param.recommended_actions = escu_contextualize, escu_investigate action.risk = 1 action.risk.param._risk_object = dest @@ -9290,67 +9264,44 @@ 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 = 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. -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 - -[ESCU - AWS Cloud Provisioning From Previously Unseen IP Address - Rule] -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.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.escu.providing_technologies = ["Microsoft Exchange"] +action.escu.analytic_story = ["Emotet Malware (TA18-201A)", "Suspicious Emails"] action.correlationsearch.enabled = 1 -action.correlationsearch.label = AWS Cloud Provisioning From Previously Unseen IP Address +action.correlationsearch.label = Email Attachments With Lots Of Spaces 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.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_ip -action.risk.param._risk_object_type = system -action.risk.param._risk_score = 30 +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_ip -alert.suppress.period = 14400s +alert.suppress.fields = src_user +alert.suppress.period = 86400s 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." +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 @@ -9361,30 +9312,79 @@ 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 +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 - Identify Systems Receiving Remote Desktop Traffic] +[ESCU - Count of Unique IPs Connecting to Ports] 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 = 2017-06-24 +action.escu.modification_date = 2017-09-13 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.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 - Identify Systems Receiving Remote Desktop 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 = ["Lateral Movement"] -description = This search counts the numbers of times the system has created remote desktop traffic +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 from datamodel=Network_Traffic where All_Traffic.dest_port=3389 by All_Traffic.dest | `drop_dm_object_name("All_Traffic")` | sort - 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 - 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 [ESCU - Get Risk Modifiers For Endpoint] action.escu = 0 @@ -9399,7 +9399,7 @@ 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 = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Splunk Enterprise Vulnerability", "Credential Dumping", "Suspicious MSHTA Activity", "DNS Amplification Attacks", "Monitor for Updates", "SamSam Ransomware", "Malicious PowerShell", "Asset Tracking", "Prohibited Traffic Allowed or Protocol Mismatch", "Splunk Enterprise Vulnerability CVE-2018-11409", "Emotet Malware (TA18-201A)", "Use of Cleartext Protocols", "Apache Struts Vulnerability", "Windows Persistence Techniques", "Account Monitoring and Controls", "Hidden Cobra Malware", "Ransomware", "Suspicious Windows Registry Activities", "Suspicious Emails", "DNS Hijacking", "Windows Log Manipulation", "Lateral Movement", "Windows Service Abuse", "Netsh Abuse", "Dynamic DNS", "Host Redirection", "Monitor Backup Solution", "Spectre And Meltdown Vulnerabilities", "Command and Control", "JBoss Vulnerability", "Monitor for Unauthorized Software", "Brand Monitoring", "Suspicious WMI Use", "Router & Infrastructure Security", "Orangeworm Attack Group", "Suspicious DNS Traffic", "Windows Privilege Escalation", "DHS Report TA18-074A", "Windows File Extension and Association Abuse", "Unusual Processes", "SQL Injection", "ColdRoot MacOS RAT", "Collection and Staging", "Disabling Security Tools", "Data Protection"] +action.escu.analytic_story = ["Possible Backdoor Activity Associated With MUDCARP Espionage Campaigns", "Splunk Enterprise Vulnerability", "Credential Dumping", "SQL Injection", "Suspicious WMI Use", "Lateral Movement", "Disabling Security Tools", "Netsh Abuse", "Emotet Malware (TA18-201A)", "Account Monitoring and Controls", "Windows Persistence Techniques", "Orangeworm Attack Group", "JBoss Vulnerability", "Splunk Enterprise Vulnerability CVE-2018-11409", "Dynamic DNS", "Unusual Processes", "Brand Monitoring", "Suspicious MSHTA Activity", "Windows Log Manipulation", "Windows Privilege Escalation", "Asset Tracking", "ColdRoot MacOS RAT", "Suspicious DNS Traffic", "Command and Control", "SamSam Ransomware", "DNS Amplification Attacks", "Router & Infrastructure Security", "Monitor for Unauthorized Software", "Collection and Staging", "Windows Service Abuse", "Suspicious Windows Registry Activities", "Host Redirection", "Suspicious Emails", "Ransomware", "Hidden Cobra Malware", "Spectre And Meltdown Vulnerabilities", "Apache Struts Vulnerability", "Monitor for Updates", "Windows File Extension and Association Abuse", "Malicious PowerShell", "Data Protection", "Monitor Backup Solution", "DHS Report TA18-074A", "Use of Cleartext Protocols", "DNS Hijacking", "Prohibited Traffic Allowed or Protocol Mismatch"] action.escu.fields_required = ["dest"] action.escu.earliest_time_offset = 604800 action.escu.latest_time_offset = 0