Prebuilt job referenceedit

These anomaly detection jobs automatically detect file system and network anomalies on your hosts. They appear in the Anomaly Detection interface of the Elastic Security app in Kibana when you have data that matches their configuration. Each job lists the type of Elastic Agent integration or Beat that collects the pertinent data. If you do not use the Elastic Agent or Beats, you must map your data to the ECS fields that are listed for each job.

For more details, see the datafeed and job definitions in the security_* and siem_* folders in GitHub.

Security: Auditbeatedit

Detect suspicious network activity and unusual processes in Auditbeat data.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

In 7.11 or later versions, use the Security: Linux jobs instead.[1]

linux_anomalous_network_activity_ecs

Identifies OS processes that do not usually use the network but have unexpected network activity, which can indicate command-and-control, lateral movement, persistence, or data exfiltration activity.

A process with unusual network activity can denote process exploitation or injection, where the process is used to run persistence mechanisms that allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized network applications.

Job details
  • Analyzes network activity logs where agent.type is auditbeat.
  • Models the occurrences of processes that cause network activity.
  • Detects network activity caused by processes that occur rarely compared to other processes (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • destination.ip
  • host.name
  • process.name
  • user.name
  • event.action
  • agent.type
linux_anomalous_network_port_activity_ecs

Identifies unusual destination port activity that can indicate command-and-control, persistence mechanism, or data exfiltration activity.

Rarely used destination port activity is generally unusual in Linux fleets, and can indicate unauthorized access or threat actor activity.

Job details
  • Analyzes network activity logs where agent.type is auditbeat.
  • Models destination port activity.
  • Detects destination port activity that occurs rarely compared to other port activities (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat (Linux)

This job is available only when you use Auditbeat to ship data. [2]

linux_anomalous_network_service

Searches for unusual listening ports that can indicate execution of unauthorized services, backdoors, or persistence mechanisms.

Job details
  • Analyzes network activity logs where agent.type is auditbeat.
  • Models listening port activity.
  • Detects listening port activity that occurs rarely compared to other port activities (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat (Linux)

This job is available only when you use Auditbeat to ship data.[2]

linux_anomalous_network_url_activity_ecs

Searches for unusual web URL requests from hosts, which can indicate malware delivery and execution.

Wget and cURL are commonly used by Linux programs to download code and data. Most of the time, their usage is entirely normal. Generally, because they use a list of URLs, they repeatedly download from the same locations. However, Wget and cURL are sometimes used to deliver Linux exploit payloads, and threat actors use these tools to download additional software and code. For these reasons, unusual URLs can indicate unauthorized downloads or threat activity.

Job details
  • Analyzes network activity logs where agent.type is auditbeat.
  • Models the occurrences of URL requests.
  • Detects a web URL request that is rare compared to other web URL requests (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat (Linux)
Required ECS fields when not using Beats
  • destination.ip
  • destination.port
  • host.name
  • process.name
  • process.title
  • agent.type
linux_anomalous_process_all_hosts_ecs

Searches for rare processes running on multiple hosts in an entire fleet or network.

This reduces the detection of false positives since automated maintenance processes usually only run occasionally on a single machine but are common to all or many hosts in a fleet.

Job details
  • Analyzes host activity logs where agent.type is auditbeat.
  • Models the occurrences of processes on all hosts.
  • Detects processes that occur rarely compared to other processes on all hosts (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
  • process.executable
  • event.action
  • agent.type
linux_anomalous_user_name_ecs

Searches for activity from users who are not normally active, which can indicate unauthorized changes, activity by unauthorized users, lateral movement, and compromised credentials.

In organizations, new usernames are not often created apart from specific types of system activities, such as creating new accounts for new employees. These user accounts quickly become active and routine.

Events from rarely used usernames can point to suspicious activity. Additionally, automated Linux fleets tend to see activity from rarely used usernames only when personnel log in to make authorized or unauthorized changes, or threat actors have acquired credentials and log in for malicious purposes. Unusual usernames can also indicate pivoting, where compromised credentials are used to try and move laterally from one host to another.

Job details
  • Analyzes host activity logs where agent.type is auditbeat.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
  • event.action
  • agent.type
linux_network_configuration_discovery

Looks for commands related to system network configuration discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used by a threat actor to engage in system network configuration discovery in order to increase their understanding of connected networks and hosts. This information may be used to shape follow-up behavior such as lateral movement or additional discovery.

Job details
  • Analyzes host activity logs where agent.type is auditbeat and process.name is commands like arp, echo, or ifconfig, for example.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.args
  • process.name
  • user.name
linux_network_connection_discovery

Looks for commands related to system network connection discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used by a threat actor to engage in system network connection discovery in order to increase their understanding of connected services and systems. This information may be used to shape follow-up behaviors such as lateral movement or additional discovery.

Job details
  • Analyzes host activity logs where agent.type is auditbeat and process.name is commands like netstat, ss, or route, for example.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.args
  • process.name
  • user.name
linux_rare_kernel_module_arguments

Looks for unusual kernel modules which are often used for stealth.

Job details
  • Analyzes host activity logs where agent.type is auditbeat and process.name is commands like insmod, kmod, or rmod, for example.
  • Models occurrences of process activity.
  • Detects processes that are rarely or unusually active compared to other processes (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.title
  • process.working_directory
  • user.name
linux_rare_metadata_process

Looks for anomalous access to the metadata service by an unusual process. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets.

Job details
  • Analyzes host activity logs where agent.type is auditbeat and destination.ip is the metadata service.
  • Models process activity.
  • Detects processes that are rarely or unusually active compared to other processes (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
linux_rare_metadata_user

Looks for anomalous access to the metadata service by an unusual user. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets.

Job details
  • Analyzes host activity logs where agent.type is auditbeat and destination.ip is the metadata service.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • user.name
linux_rare_sudo_user

Looks for sudo activity from an unusual user context.

Job details
  • Analyzes host activity logs where agent.type is auditbeat, process.name is sudo, and event.action is executed.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.args
  • process.name
  • user.name
linux_rare_user_compiler

Looks for compiler activity by a user context which does not normally run compilers. This can be ad-hoc software changes or unauthorized software deployment. This can also be due to local privilege elevation via locally run exploits or malware activity.

Job details
  • Analyzes host activity logs where agent.type is auditbeat and process.name is commands like compile, make, or gcc, for example.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.title
  • process.working_directory
  • user.name
linux_system_information_discovery

Looks for commands related to system information discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used to engage in system information discovery in order to gather detailed information about system configuration and software versions. This may be a precursor to selection of a persistence mechanism or a method of privilege elevation.

Job details
  • Analyzes host activity logs where agent.type is auditbeat and process.name is commands like cat, grep, or hostname, for example.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.args
  • process.name
  • user.name
linux_system_process_discovery

Looks for commands related to system process discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used to engage in system process discovery in order to increase their understanding of software applications running on a target host or network. This may be a precursor to selection of a persistence mechanism or a method of privilege elevation.

Job details
  • Analyzes host activity logs where agent.type is auditbeat and process.name is commands like ps or top, for example.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.args
  • process.name
  • user.name
linux_system_user_discovery

Looks for commands related to system user or owner discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used to engage in system owner or user discovery in order to identify currently active or primary users of a system. This may be a precursor to additional discovery, credential dumping or privilege elevation activity.

Job details
  • Analyzes host activity logs where agent.type is auditbeat and process.name is commands like users, whoami, or who, for example.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.args
  • process.name
  • user.name
rare_process_by_host_linux_ecs

Identifies rare processes that do not usually run on individual hosts, which can indicate execution of unauthorized services, malware, or persistence mechanisms.

Processes are considered rare when they only run occasionally as compared with other processes running on the host.

Job details
  • Analyzes host activity logs where agent.type is auditbeat (Linux).
  • Models occurrences of process activities on the host.
  • Detects unusually rare processes compared to other processes on the host (using the rare function).
Required Beats or Elastic Agent integrations
  • Auditbeat
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
  • event.action
  • agent.type

Security: Auditbeat authenticationedit

Detect suspicious authentication events in Auditbeat data.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

suspicious_login_activity_ecs

Identifies an unusually high number of authentication attempts.

Job details
  • Analyzes host activity logs where agent.type is auditbeat.
  • Models occurrences of authentication attempts (partition_field_name is host.name).
  • Detects unusually high number of authentication attempts (using the high_non_zero_count function).
Required Beats or Elastic Agent integrations
  • Auditbeat (Linux)
Required ECS fields when not using Beats
  • source.ip
  • host.name
  • user.name
  • event.category
  • agent.type

Security: Authenticationedit

Detect anomalous activity in your ECS-compatible authentication logs.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

By default, when you create these job in the Elastic Security app, it uses a data view that applies to multiple indices. To get the same results if you use the Machine Learning app, create a similar data view then select it in the job wizard.

auth_high_count_logon_events

Looks for an unusually large spike in successful authentication events. This can be due to password spraying, user enumeration or brute force activity.

Job details
  • Detects anomalies where the number of events is unusually high and ignores cases where the count is zero (using the high_non_zero_count function).
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
Required ECS fields
  • event.category
  • event.outcome
auth_high_count_logon_events_for_a_source_ip

Looks for an unusually large spike in successful authentication events from a particular source IP address. This can be due to password spraying, user enumeration or brute force activity.

Job details
  • Detects anomalies where the number of events by source IP is unusually high and ignores cases where the count is zero (using the high_non_zero_count function).
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
Required ECS fields
  • event.category
  • event.outcome
  • source.ip
  • user.name
  • winlog.event_data.LogonType
auth_high_count_logon_fails

Looks for an unusually large spike in authentication failure events. This can be due to password spraying, user enumeration or brute force activity and may be a precursor to account takeover or credentialed access.

Job details
  • Detects anomalies where the number of events is unusually high and ignores cases where the count is zero (using the high_non_zero_count function).
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
Required ECS fields
  • event.category
  • event.outcome
auth_rare_hour_for_a_user

Looks for a user logging in at a time of day that is unusual for the user. This can be due to credentialed access via a compromised account when the user and the threat actor are in different time zones. In addition, unauthorized user activity often takes place during non-business hours.

Job details
  • Detects anomalies where events happen at unusual times for a user (using the time_of_day function).
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
Required ECS fields
  • event.category
  • event.outcome
  • source.ip
  • user.name
auth_rare_source_ip_for_a_user

Looks for a user logging in from an IP address that is unusual for the user. This can be due to credentialed access via a compromised account when the user and the threat actor are in different locations. An unusual source IP address for a username could also be due to lateral movement when a compromised account is used to pivot between hosts.

Job details
  • For each user, detects rare source.ip values (using the rare function).
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
Required ECS fields
  • event.category
  • event.outcome
auth_rare_user

Looks for an unusual user name in the authentication logs. An unusual user name is one way of detecting credentialed access by means of a new or dormant user account. A user account that is normally inactive, because the user has left the organization, which becomes active, may be due to credentialed access using a compromised account password. Threat actors will sometimes also create new users as a means of persisting in a compromised web application.

Job details
  • Detects unusually rare user.name values (using the rare function).
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
Required ECS fields
  • event.category
  • event.outcome
  • source.ip
  • user.name

Security: CloudTrailedit

Detect suspicious activity recorded in your CloudTrail logs.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

high_distinct_count_error_message

Looks for a spike in the rate of an error message. These spikes might simply indicate an impending service failure but they can also be byproducts of attempted or successful persistence, privilege escalation, defense evasion, discovery, lateral movement, or collection activity by a threat actor.

Job details
  • Detects anomalies where the number of distinct values in the aws.cloudtrail.error_message field is unusual (using the high_distinct_count function).
Required Beats or Elastic Agent integrations
  • Filebeat
Required ECS fields when not using Beats
  • source.geo.city_name
  • source.ip
rare_error_code

Looks for unusual errors. Rare and unusual errors might simply indicate an impending service failure but they can also be byproducts of attempted or successful persistence, privilege escalation, defence evasion, discovery, lateral movement, or collection activity by a threat actor.

Job details
  • Detects aws.cloudtrail.error_code values that have never or rarely occurred before (using the rare function).
Required Beats or Elastic Agent integrations
  • Filebeat
Required ECS fields when not using Beats
  • source.geo.city_name
  • source.ip
rare_method_for_a_city

Looks for AWS API calls that—​while not inherently suspicious or abnormal—​are sourcing from a geolocation (city) that is unusual. These calls can be the result of compromised credentials or keys.

Job details
  • For each city, detects rare event.action values (using the rare function).
Required Beats or Elastic Agent integrations
  • Filebeat
Required ECS fields when not using Beats
  • event.action
  • source.geo.city_name
  • source.ip
rare_method_for_a_country

Looks for AWS API calls that—​while not inherently suspicious or abnormal—​are sourcing from a geolocation (country) that is unusual. These calls can be the result of compromised credentials or keys.

Job details
  • For each country, detects rare event.action values (using the rare function).
Required Beats or Elastic Agent integrations
  • Filebeat
Required ECS fields when not using Beats
  • event.action
  • source.geo.country_iso_code
  • source.ip
rare_method_for_a_username

Looks for AWS API calls that—​while not inherently suspicious or abnormal—​are sourcing from a user context that does not normally call the method. These calls can be the result of compromised credentials or keys as someone uses a valid account to persist, move laterally, or exfil data.

Job details
  • For each user, detects rare event.action values (using the rare function).
Required Beats or Elastic Agent integrations
  • Filebeat
Required ECS fields when not using Beats
  • event.action
  • source.geo.city_name
  • source.ip
  • user.name

Security: Linuxedit

Detect suspicious activity using ECS Linux events.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

In 7.11 or later versions, use these jobs instead of the Security: Auditbeat jobs.[1]

v2_linux_anomalous_network_port_activity_ecs

Identifies unusual destination port activity that can indicate command-and-control, persistence mechanism, or data exfiltration activity.

Rarely used destination port activity is generally unusual in Linux fleets, and can indicate unauthorized access or threat actor activity.

Job details
  • Models destination port activity.
  • Detects destination port activity that occurs rarely compared to other port activities (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Auditbeat
Required ECS fields
  • destination.ip
  • destination.port
  • event.category
  • event.type
  • host.name
  • host.os.family
  • host.os.type
  • process.name
  • user.name
v2_linux_anomalous_process_all_hosts_ecs

Looks for processes that are unusual to all Linux hosts. Such unusual processes may indicate unauthorized services, malware, or persistence mechanisms.

This reduces the detection of false positives since automated maintenance processes usually only run occasionally on a single machine but are common to all or many hosts in a fleet.

Job details
  • Models the occurrences of processes on all Linux hosts.
  • Detects processes that occur rarely compared to other processes on all Linux hosts (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Auditbeat
Required ECS fields
  • event.category
  • event.type
  • host.name
  • host.os.family
  • host.os.type
  • process.name
  • user.name
v2_linux_anomalous_user_name_ecs

Searches for activity from users who are not normally active, which can indicate unauthorized changes, activity by unauthorized users, lateral movement, and compromised credentials.

In organizations, new usernames are not often created apart from specific types of system activities, such as creating new accounts for new employees. These user accounts quickly become active and routine.

Events from rarely used usernames can point to suspicious activity. Additionally, automated Linux fleets tend to see activity from rarely used usernames only when personnel log in to make authorized or unauthorized changes, or threat actors have acquired credentials and log in for malicious purposes. Unusual usernames can also indicate pivoting, where compromised credentials are used to try and move laterally from one host to another.

Job details
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Auditbeat
Required ECS fields
  • event.category
  • event.type
  • host.name
  • host.os.family
  • host.os.type
  • process.name
  • user.name
v2_linux_rare_metadata_process

Looks for anomalous access to the metadata service by an unusual process. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets.

Job details
  • Analyzes host activity logs where destination.ip is the metadata service
  • Models process activity.
  • Detects processes that are rarely or unusually active compared to other processes (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Auditbeat
Required ECS fields
  • destination.ip
  • host.name
  • host.os.family
  • host.os.type
  • process.name
  • user.name
v2_linux_rare_metadata_user

Looks for anomalous access to the metadata service by an unusual user. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets.

Job details
  • Analyzes host activity logs where destination.ip is the metadata service
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Auditbeat
Required ECS fields
  • destination.ip
  • host.name
  • host.os.family
  • host.os.type
  • user.name
v2_rare_process_by_host_linux_ecs

Looks for processes that are unusual to a particular Linux host. Such unusual processes might indicate unauthorized services, malware, or persistence mechanisms.

Processes are considered rare when they only run occasionally as compared with other processes running on the host.

Job details
  • Models occurrences of process activities on the host.
  • Detects unusually rare processes compared to other processes on the host (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Auditbeat
Required ECS fields
  • event.category
  • event.type
  • host.name
  • host.os.family
  • host.os.type
  • process.name
  • user.name

Security: Networkedit

Detect anomalous network activity in your ECS-compatible network logs.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

By default, when you create these jobs in the Elastic Security app, it uses a data view that applies to multiple indices. To get the same results if you use the Machine Learning app, create a similar data view then select it in the job wizard.

high_count_by_destination_country

Looks for an unusually large spike in network activity to one destination country in the network logs. This could be due to unusually large amounts of reconnaissance or enumeration traffic. Data exfiltration activity may also produce such a surge in traffic to a destination country which does not normally appear in network traffic or business work-flows. Malware instances and persistence mechanisms may communicate with command-and-control (C2) infrastructure in their country of origin, which may be an unusual destination country for the source network.

Job details
  • Analyzes network activity logs where event.category is network.
  • Detects unusually high number of events by country (using the high_non_zero function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Filebeat
  • Packetbeat
Required ECS fields
  • destination.as.organization.name
  • destination.geo.country_name
  • destination.ip
  • event.category
  • source.ip
high_count_network_denies

Looks for an unusually large spike in network traffic that was denied by network access control lists (ACL) or firewall rules. Such a burst of denied traffic is usually either a misconfigured application or firewall, or suspicious or malicious activity. Unsuccessful attempts at network transit, in order to connect to command-and-control (C2), or engage in data exfiltration, may produce a burst of failed connections. This could also be due to unusually large amounts of reconnaissance or enumeration traffic. Denial-of-service attacks or traffic floods may also produce such a surge in traffic.

Job details
  • Analyzes network activity logs where event.category is network and event.outcome is deny.
  • Detects unusually high numbers of events (using the high_count function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Filebeat
  • Packetbeat
Required ECS fields
  • destination.as.organization.name
  • destination.geo.country_name
  • destination.port
  • event.category
  • event.outcome
  • source.ip
high_count_network_events

Looks for an unusually large spike in network traffic. Such a burst of traffic, if not caused by a surge in business activity, can be due to suspicious or malicious activity. Large-scale data exfiltration may produce a burst of network traffic; this could also be due to unusually large amounts of reconnaissance or enumeration traffic. Denial-of-service attacks or traffic floods may also produce such a surge in traffic.

Job details
  • Analyzes network activity logs where event.category is network.
  • Detects unusually high numbers of events (using the high_count function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Filebeat
  • Packetbeat
Required ECS fields
  • destination.as.organization.name
  • destination.geo.country_name
  • destination.port
  • event.category
  • source.ip
rare_destination_country

Looks for an unusual destination country name in the network logs. This can be due to initial access, persistence, command-and-control, or exfiltration activity. For example, when a user clicks on a link in a phishing email or opens a malicious document, a request may be sent to download and run a payload from a server in a country which does not normally appear in network traffic or business work-flows. Malware instances and persistence mechanisms may communicate with command-and-control (C2) infrastructure in their country of origin, which may be an unusual destination country for the source network.

Job details
  • Analyzes network activity logs where event.category is network.
  • Detects activity that is rare by country name (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Filebeat
  • Packetbeat
Required ECS fields
  • destination.geo.country_name
  • event.category

Security: Packetbeatedit

Detect suspicious network activity in Packetbeat data.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

packetbeat_dns_tunneling

Searches for unusually large numbers of DNS queries for a single top-level DNS domain, which is often used for DNS tunneling.

DNS tunneling can be used for command-and-control, persistence, or data exfiltration activity. For example, dnscat tends to generate many DNS questions for a top-level domain as it uses the DNS protocol to tunnel data.

Job details
  • Analyzes network activity logs where agent.type is packetbeat.
  • Models occurrences of DNS activity.
  • Detects unusual DNS activity (using the high_info_content function).
Required Beats or Elastic Agent integrations
  • Packetbeat (Windows and Linux)
Required ECS fields when not using Beats
  • destination.ip
  • dns.question.registered_domain
  • host.name
  • dns.question.name
  • event.dataset
  • agent.type

This job uses the Packetbeat dns.question.etld_plus_one field, which is not defined in ECS. Instead, map your network data to the dns.question.registered_domain ECS field.

packetbeat_rare_dns_question

Searches for rare and unusual DNS queries that indicate network activity with unusual domains is about to occur. This can be due to initial access, persistence, command-and-control, or exfiltration activity.

For example, when a user clicks on a link in a phishing email or opens a malicious document, a request may be sent to download and run a payload from an uncommon domain. When malware is already running, it may send requests to an uncommon DNS domain the malware uses for command-and-control communication.

Job details
  • Analyzes network activity logs where agent.type is packetbeat.
  • Models occurrences of DNS activity.
  • Detects DNS activity that is rare compared to other DNS activities (using the rare function).
Required Beats or Elastic Agent integrations
  • Packetbeat (Windows and Linux)
Required ECS fields when not using Beats
  • host.name
  • dns.question.name
  • dns.question.type
  • event.dataset
  • agent.type
packetbeat_rare_server_domain

Searches for rare and unusual DNS queries that indicate network activity with unusual domains is about to occur. This can be due to initial access, persistence, command-and-control, or exfiltration activity.

For example, when a user clicks on a link in a phishing email or opens a malicious document, a request may be sent to download and run a payload from an uncommon HTTP or TLS server. When malware is already running, it may send requests to an uncommon DNS domain the malware uses for command-and-control communication.

Job details
  • Analyzes network activity logs where agent.type is packetbeat.
  • Models HTTP or TLS domain activity.
  • Detects HTTP or TLS domain activity that is rare compared to other activities (using the rare function).
Required Beats or Elastic Agent integrations
  • Packetbeat (Windows and Linux)
Required ECS fields when not using Beats
  • destination.ip
  • source.ip
  • host.name
  • server.domain
  • agent.type
packetbeat_rare_urls

Searches for rare and unusual URLs that indicate unusual web browsing activity. This can be due to initial access, persistence, command-and-control, or exfiltration activity.

For example, in a strategic web compromise or watering hole attack, when a trusted website is compromised to target a particular sector or organization, targeted users may receive emails with uncommon URLs for trusted websites. These URLs can be used to download and run a payload. When malware is already running, it may send requests to uncommon URLs on trusted websites the malware uses for command-and-control communication. When rare URLs are observed being requested for a local web server by a remote source, these can be due to web scanning, enumeration or attack traffic, or they can be due to bots and web scrapers which are part of common Internet background traffic.

Job details
  • Analyzes network activity logs where agent.type is packetbeat.
  • Models occurrences of web browsing URL activity.
  • Detects URL activity that rarely occurs compared to other URL activities (using the rare function).
Required Beats or Elastic Agent integrations
  • Packetbeat (Windows and Linux)
Required ECS fields when not using Beats
  • destination.ip
  • host.name
  • url.full
  • agent.type
packetbeat_rare_user_agent

Searches for rare and unusual user agents that indicate web browsing activity by an unusual process other than a web browser. This can be due to persistence, command-and-control, or exfiltration activity. Uncommon user agents coming from remote sources to local destinations are often the result of scanners, bots, and web scrapers, which are part of common internet background traffic.

Much of this is noise, but more targeted attacks on websites using tools like Burp or SQLmap can sometimes be discovered by spotting uncommon user agents. Uncommon user agents in traffic from local sources to remote destinations can be any number of things, including harmless programs like weather monitoring or stock-trading programs. However, uncommon user agents from local sources can also be due to malware or scanning activity.

Job details
  • Analyzes network activity logs where agent.type is packetbeat.
  • Models occurrences of HTTP user agent activity.
  • Detects HTTP user agent activity that occurs rarely compared to other HTTP user agent activities (using the rare function).
Required Beats or Elastic Agent integrations
  • Packetbeat (Windows and Linux)
Required ECS fields when not using Beats
  • destination.ip
  • host.name
  • event.dataset
  • user_agent.original
  • agent.type

Security: Windowsedit

Detects suspicious activity using ECS Windows events.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

If there are additional requirements such as installing the Windows System Monitor (Sysmon) or auditing process creation in the Windows security event log, they are listed for each job.

In 7.11 or later versions, use these jobs instead of the Security: Winlogbeat jobs.[3]

v2_rare_process_by_host_windows_ecs

Detects unusually rare processes on Windows hosts, which can indicate execution of unauthorized services, malware, or persistence mechanisms.

Processes are considered rare when they only run occasionally as compared with other processes running on the host.

Job details
  • Models occurrences of process activities on the host.
  • Detects unusually rare processes compared to other processes on the host (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Winlogbeat, colecting data from the Windows System Monitor (Sysmon) or the Windows security event log

    If you collect data from the Windows security event log and you configure it to audit process creation, this job can analyze the 4688 events that occur every time a new process starts.[4]

Required ECS fields
  • event.category
  • event.type
  • host.name
  • host.os.family
  • host.os.type
  • process.name
  • user.name
v2_windows_anomalous_network_activity_ecs

Looks for unusual processes using the network which could indicate command-and- control, lateral movement, persistence, or data exfiltration activity.

A process with unusual network activity can denote process exploitation or injection, where the process is used to run persistence mechanisms that allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized network applications.

Job details
  • Models the occurrences of processes that cause network activity.
  • Detects network activity caused by processes that occur rarely compared to other processes (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Winlogbeat, collecting data from Windows System Monitor (Sysmon)
Required ECS fields
  • destination.ip
  • event.category
  • event.type
  • host.name
  • host.os.family
  • host.os.type
  • process.name
  • user.name
v2_windows_anomalous_path_activity_ecs

Looks for activity in unusual paths, which might indicate execution of malware or persistence mechanisms.

Windows payloads often execute from user profile paths. In corporate Windows environments, software installation is centrally managed and it is unusual for programs to be executed from user or temporary directories. Processes executed from these locations can denote that a user downloaded software directly from the internet or a malicious script/macro executed malware.

Job details
  • Models occurrences of processes in paths.
  • Detects activity in unusual paths (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Winlogbeat, collecting data from the Windows System Monitor (Sysmon)
Required ECS fields
  • event.category
  • event.type
  • host.os.family
  • host.name
  • host.os.type
  • process.name
  • process.working_directory
  • user.name
v2_windows_anomalous_process_all_hosts_ecs

Looks for processes that are unusual to all Windows hosts. Such unusual processes may indicate execution of unauthorized services, malware, or persistence mechanisms.

This reduces the detection of false positives since automated maintenance processes usually only run occasionally on a single machine but are common to all or many hosts in a fleet.

Job details
  • Models the occurrences of processes on all hosts.
  • Detects processes that occur rarely compared to other processes on all hosts (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Winlogbeat, colecting data from the Windows System Monitor (Sysmon) or from the Windows security event log with process creation auditing enabled.

    If you collect data from the Windows security event log and you configure it to audit process creation, this job can analyze the 4688 events that occur every time a new process starts.[4]

Required ECS fields
  • event.category
  • event.type
  • host.name
  • host.os.family
  • process.executable
  • process.name
  • user.name
v2_windows_anomalous_process_creation

Identifies unusual process relationships that can indicate malware execution or persistence mechanisms.

Malicious scripts often call on other applications and processes as part of their exploit payload. For example, when a malicious Office document runs scripts as part of an exploit payload, Excel or Word may start a script interpreter process, which, in turn, runs a script that downloads and executes malware. Another common scenario is Outlook running an unusual process when malware is downloaded in an email.

Monitoring and identifying anomalous process relationships is an excellent way of detecting new and emerging malware that is not yet recognized by anti-virus scanners.

Job details
  • Models occurrences of process creation activities (partition_field_name is process.parent.name).
  • Detects process relationships that are rare compared to other process relationships (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Winlogbeat, collecting data from the Windows System Monitor (Sysmon) or the Windows security event log

    If you collect data from the Windows security event log and you configure it to audit process creation, this job can analyze the 4688 events that occur every time a new process starts.[4]

Required ECS fields
  • event.category
  • event.type
  • host.name
  • host.os.family
  • host.os.type
  • process.name
  • process.parent.name
  • user.name
v2_windows_anomalous_user_name_ecs

Searches for activity from users who are not normally active, which can indicate unauthorized changes, activity by unauthorized users, lateral movement, and compromised credentials.

In organizations, new usernames are not often created apart from specific types of system activities, such as creating new accounts for new employees. These user accounts quickly become active and routine.

Events from rarely used usernames can point to suspicious activity. Additionally, automated Linux fleets tend to see activity from rarely used usernames only when personnel log in to make authorized or unauthorized changes, or threat actors have acquired credentials and log in for malicious purposes. Unusual usernames can also indicate pivoting, where compromised credentials are used to try and move laterally from one host to another.

Job details
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
  • Works on ECS compatible events across multiple indices
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Winlogbeat, collecting data from the Windows System Monitor (Sysmon) or the Windows security event log

    If you collect data from the Windows security event log and you configure it to audit process creation, this job can analyze the 4688 events that occur every time a new process starts.[4]

Required ECS fields
  • event.category
  • event.type
  • host.name
  • host.os.family
  • host.os.type
  • process.name
  • user.name
v2_windows_rare_metadata_process

Looks for anomalous access to the metadata service by an unusual process. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets.

Job details
  • Analyzes host activity logs where destination.ip is the metadata service.
  • Models process activity.
  • Detects processes that are rarely or unusually active compared to other processes (using the rare function).
  • Works on ECS compatible events across multiple indices.
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Winlogbeat, collecting data from the Windows System Monitor (Sysmon)
Required ECS fields
  • destination.ip
  • host.name
  • host.os.family
  • process.name
  • user.name
v2_windows_rare_metadata_user

Looks for anomalous access to the metadata service by an unusual user. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets.

Job details
  • Analyzes host activity logs where destination.ip is the metadata service.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Elastic Endpoint integration
  • Winlogbeat, collecting data from the Windows System Monitor (Sysmon)
Required ECS fields
  • destination.ip
  • host.name
  • host.os.family
  • user.name

Security: Winlogbeatedit

Detect unusual processes and network activity in Winlogbeat data.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

In 7.11 or later versions, use the Security: Windows jobs instead.[3]

rare_process_by_host_windows_ecs

Identifies rare processes that do not usually run on individual hosts, which can indicate execution of unauthorized services, malware, or persistence mechanisms.

Processes are considered rare when they only run occasionally as compared with other processes running on the host.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat.
  • Models occurrences of process activities on the host.
  • Detects unusually rare processes compared to other processes on the host (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
  • event.action
  • agent.type
windows_anomalous_network_activity_ecs

Identifies OS processes that do not usually use the network but have unexpected network activity, which can indicate command-and-control, lateral movement, persistence, or data exfiltration activity.

A process with unusual network activity can denote process exploitation or injection, where the process is used to run persistence mechanisms that allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized network applications.

Job details
  • Analyzes network activity logs where agent.type is winlogbeat.
  • Models the occurrences of processes that cause network activity.
  • Detects network activity caused by processes that occur rarely compared to other processes (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat
Required ECS fields when not using Beats
  • destination.ip
  • host.name
  • process.name
  • user.name
  • event.action
  • agent.type
windows_anomalous_path_activity_ecs

Identifies processes started from atypical folders in the file system, which might indicate malware execution or persistence mechanisms.

In corporate Windows environments, software installation is centrally managed and it is unusual for programs to be executed from user or temporary directories. Processes executed from these locations can denote that a user downloaded software directly from the internet or a malicious script/macro executed malware.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat.
  • Models occurrences of processes in paths.
  • Detects activity in unusual paths (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat (Windows)
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
  • process.working_directory
  • event.action
  • agent.type
windows_anomalous_process_all_hosts_ecs

Searches for rare processes running on multiple hosts in an entire fleet or network.

This reduces the detection of false positives since automated maintenance processes usually only run occasionally on a single machine but are common to all or many hosts in a fleet.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat (Windows).
  • Models the occurrences of processes on all hosts.
  • Detects processes that occur rarely compared to other processes on all hosts (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
  • process.executable
  • event.action
  • agent.type
windows_anomalous_process_creation

Identifies unusual parent-child process relationships that can indicate malware execution or persistence mechanisms.

Malicious scripts often call on other applications and processes as part of their exploit payload. For example, when a malicious Office document runs scripts as part of an exploit payload, Excel or Word may start a script interpreter process, which, in turn, runs a script that downloads and executes malware. Another common scenario is Outlook running an unusual process when malware is downloaded in an email.

Monitoring and identifying anomalous process relationships is an excellent way of detecting new and emerging malware that is not yet recognized by anti-virus scanners.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat.
  • Models occurrences of process creation activities (partition_field_name is process.parent.name).
  • Detects process relationships that are rare compared to other process relationships (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat (Windows)
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
  • process.parent.name
  • event.action
  • agent.type
windows_anomalous_script

Searches for PowerShell scripts with unusual data characteristics, such as obfuscation, that may be a characteristic of malicious PowerShell script text blocks.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat.
  • Models occurrences of PowerShell script activities.
  • Detects unusual PowerShell script execution compared to other PowerShell script activities (using the high_info_content function).
Required Beats or Elastic Agent integrations
  • Winlogbeat (Windows)

This job is available only when you use Winlogbeat to ship data.[2]

windows_anomalous_service

Searches for unusual Windows services that can indicate execution of unauthorized services, malware, or persistence mechanisms.

In corporate Windows environments, hosts do not generally run many rare or unique services. This job helps detect malware and persistence mechanisms that have been installed and run as a service.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat.
  • Models occurrences of Windows service activities.
  • Detects Windows service activities that occur rarely compared to other Windows service activities (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat (Windows)

This job is available only when you use Winlogbeat to ship data.[2]

windows_anomalous_user_name_ecs

Searches for activity from users who are not normally active, which can indicate unauthorized changes, activity by unauthorized users, lateral movement, and compromised credentials.

In organizations, new usernames are not often created apart from specific types of system activities, such as creating new accounts for new employees. These user accounts quickly become active and routine.

Events from rarely used usernames can point to suspicious activity. Additionally, automated Linux fleets tend to see activity from rarely used usernames only when personnel log in to make authorized or unauthorized changes, or threat actors have acquired credentials and log in for malicious purposes. Unusual usernames can also indicate pivoting, where compromised credentials are used to try and move laterally from one host to another.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat (Windows).
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
  • event.action
  • agent.type
windows_rare_metadata_process

Looks for anomalous access to the metadata service by an unusual process. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat (Windows) and destination.ip is the metadata service.
  • Models process activity.
  • Detects processes that are rarely or unusually active compared to other processes (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat (Windows)
Required ECS fields when not using Beats
  • host.name
  • process.name
  • user.name
windows_rare_metadata_user

Looks for anomalous access to the metadata service by an unusual user. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat (Windows) and destination.ip is the metadata service.
  • Models user activity.
  • Detects users that are rarely or unusually active compared to other users (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat (Windows)
Required ECS fields when not using Beats
  • host.name
  • user.name
windows_rare_user_runas_event

Searches for unusual user context switches using the runas command or similar techniques, which can indicate account takeover or privilege escalation using compromised accounts. Privilege elevation using tools like runas is more common for domain and network administrators than professionals who are not members of the technology department.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat.
  • Models occurrences of user context switches.
  • Detects user context switches that occur rarely compared to other user context switches (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat (Windows)
Required ECS fields when not using Beats
  • process.name
  • host.name
  • user.name
  • event.code
  • agent.type

Security: Winlogbeat authenticationedit

Detect suspicious authentication events in Winlogbeat data.

In the Machine Learning app, these configurations are available only when data exists that matches the query specified in the manifest file. In the Elastic Security app, it looks in the data view specified in the securitySolution:defaultIndex advanced setting for data that matches the query.

windows_rare_user_type10_remote_login

Searches for unusual remote desktop protocol (RDP) logins, which can indicate account takeover or credentialed persistence using compromised accounts. RDP attacks, such as BlueKeep, also tend to use unusual usernames.

Job details
  • Analyzes host activity logs where agent.type is winlogbeat.
  • Models occurrences of user remote login activities.
  • Detects user remote login activities that occur rarely compared to other user remote login activities (using the rare function).
Required Beats or Elastic Agent integrations
  • Winlogbeat (Windows)

This job is available only when you use Winlogbeat to ship data.[2]

[1] If you cannot upgrade all your Beats to version 7.11 or later and you have both Security: Linux and Security: Auditbeat jobs running, you can avoid duplication by stopping the following jobs: linux_anomalous_network_activity_ecs, linux_anomalous_network_port_activity_ecs, linux_anomalous_process_all_hosts_ecs, linux_anomalous_user_name_ecs, linux_rare_metadata_process, linux_rare_metadata_user, rare_process_by_host_linux_ecs.
[2] Some jobs use fields that are not ECS-compliant. These jobs are available only when you use Beats or the Elastic Agent to ship data.
[3] If you cannot upgrade all your Beats to version 7.11 or later and you have both Security:Windows jobs and Security:Winlogbeat jobs running, you can avoid duplication by stopping the following jobs: rare_process_by_host_windows_ecs, windows_anomalous_network_activity_ecs, windows_anomalous_path_activity_ecs, windows_anomalous_process_all_hosts_ecs, windows_anomalous_process_creation, windows_anomalous_user_name_ecs, windows_rare_metadata_process, windows_rare_metadata_user
[4] The Windows security 4688 events have event.category: process, event.type: start, and event.provider: Microsoft-Windows-Security-Auditing. The following jobs can use these events: v2_rare_process_by_host_windows_ecs, v2_windows_anomalous_user_name_ecs, v2_windows_anomalous_process_all_hosts_ecs, and v2_windows_anomalous_process_creation. The Windows security event log cannot be used as a data source for jobs that pertain to network events since it does not contain that type of information. Network events can be collected by the Elastic Endpoint integration, by Winlogbeat from the Windows System Monitor, or by another ECS-compatible Windows agent.