diff --git a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_network_activity.json b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_network_activity.json index fe248a6c1e23ea..41f38173dba33b 100644 --- a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_network_activity.json +++ b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_network_activity.json @@ -20,5 +20,6 @@ "ML" ], "type": "machine_learning", + "note": "### Investigating Unusual Network Activity ###\nSignals from this rule indicate the presence of network activity from a Linux process for which network activity is rare and unusual. Here are some possible avenues of investigation:\n- Consider the IP addresses and ports. Are these used by normal but infrequent network workflows? Are they expected or unexpected? \n- If the destination IP address is remote or external, does it associate with an expected domain, organization or geography? Note: avoid interacting directly with suspected malicious IP addresses.\n- Consider the user as identified by the username field. Is this network activity part of an expected workflow for the user who ran the program?\n- Examine the history of execution. If this process manifested only very recently, it might be part of a new software package. If it has a consistent cadence - for example if it runs monthly or quarterly - it might be part of a monthly or quarterly business or maintenance process.\n- Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.", "version": 1 -} \ No newline at end of file +} diff --git a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_process_all_hosts.json b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_process_all_hosts.json index d15c4fc7943782..103171bcdfe501 100644 --- a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_process_all_hosts.json +++ b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_process_all_hosts.json @@ -20,5 +20,6 @@ "ML" ], "type": "machine_learning", + "note": "### Investigating an Unusual Linux Process ###\nSignals from this rule indicate the presence of a Linux process that is rare and unusual for all of the monitored Linux hosts for which Auditbeat data is available. Here are some possible avenues of investigation:\n- Consider the user as identified by the username field. Is this program part of an expected workflow for the user who ran this program on this host?\n- Examine the history of execution. If this process manifested only very recently, it might be part of a new software package. If it has a consistent cadence - for example if it runs monthly or quarterly - it might be part of a monthly or quarterly business process.\n- Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.", "version": 1 -} \ No newline at end of file +} diff --git a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_user_name.json b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_user_name.json index 2f33948b0a93e4..6642bb5d73fbdd 100644 --- a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_user_name.json +++ b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/linux_anomalous_user_name.json @@ -20,5 +20,6 @@ "ML" ], "type": "machine_learning", + "note": "### Investigating an Unusual Linux User ###\nSignals from this rule indicate activity for a Linux user name that is rare and unusual. Here are some possible avenues of investigation:\n- Consider the user as identified by the username field. Is this program part of an expected workflow for the user who ran this program on this host? Could this be related to troubleshooting or debugging activity by a developer or site reliability engineer?\n- Examine the history of user activity. If this user manifested only very recently, it might be a service account for a new software package. If it has a consistent cadence - for example if it runs monthly or quarterly - it might be part of a monthly or quarterly business process.\n- Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks that the user is performing.", "version": 1 -} \ No newline at end of file +} diff --git a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/rare_process_by_host_linux.json b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/rare_process_by_host_linux.json index f071677ae8d330..8ae1b84aaf1997 100644 --- a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/rare_process_by_host_linux.json +++ b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/rare_process_by_host_linux.json @@ -20,5 +20,6 @@ "ML" ], "type": "machine_learning", + "note": "### Investigating an Unusual Linux Process ###\nSignals from this rule indicate the presence of a Linux process that is rare and unusual for the host it ran on. Here are some possible avenues of investigation:\n- Consider the user as identified by the username field. Is this program part of an expected workflow for the user who ran this program on this host?\n- Examine the history of execution. If this process manifested only very recently, it might be part of a new software package. If it has a consistent cadence - for example if it runs monthly or quarterly - it might be part of a monthly or quarterly business process.\n- Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.", "version": 1 -} \ No newline at end of file +} diff --git a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/rare_process_by_host_windows.json b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/rare_process_by_host_windows.json index 5e0050c6c25ec9..879cee388f5ddf 100644 --- a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/rare_process_by_host_windows.json +++ b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/rare_process_by_host_windows.json @@ -20,5 +20,6 @@ "Windows" ], "type": "machine_learning", + "note": "### Investigating an Unusual Windows Process ###\nSignals from this rule indicate the presence of a Windows process that is rare and unusual for the host it ran on. Here are some possible avenues of investigation:\n- Consider the user as identified by the username field. Is this program part of an expected workflow for the user who ran this program on this host?\n- Examine the history of execution. If this process manifested only very recently, it might be part of a new software package. If it has a consistent cadence - for example if it runs monthly or quarterly - it might be part of a monthly or quarterly business process.\n- Examine the process metadata like the values of the Company, Description and Product fields which may indicate whether the program is associated with an expected software vendor or package. \n- Examine arguments and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.\n- Consider the same for the parent process. If the parent process is a legitimate system utility or service, this could be related to software updates or system management. If the parent process is something user-facing like an Office application, this process could be more suspicious.\n- If you have file hash values in the event data, and you suspect malware, you can optionally run a search for the file hash to see if the file is identified as malware by anti-malware tools. ", "version": 1 -} \ No newline at end of file +} diff --git a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_network_activity.json b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_network_activity.json index ca18fe95b1fc1a..1092bcb20bcc35 100644 --- a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_network_activity.json +++ b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_network_activity.json @@ -20,5 +20,6 @@ "Windows" ], "type": "machine_learning", + "note": "### Investigating Unusual Network Activity ###\nSignals from this rule indicate the presence of network activity from a Windows process for which network activity is very unusual. Here are some possible avenues of investigation:\n- Consider the IP addresses, protocol and ports. Are these used by normal but infrequent network workflows? Are they expected or unexpected? \n- If the destination IP address is remote or external, does it associate with an expected domain, organization or geography? Note: avoid interacting directly with suspected malicious IP addresses.\n- Consider the user as identified by the username field. Is this network activity part of an expected workflow for the user who ran the program?\n- Examine the history of execution. If this process manifested only very recently, it might be part of a new software package. If it has a consistent cadence - for example if it runs monthly or quarterly - it might be part of a monthly or quarterly business process.\n- Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.\n- Consider the same for the parent process. If the parent process is a legitimate system utility or service, this could be related to software updates or system management. If the parent process is something user-facing like an Office application, this process could be more suspicious.\n- If you have file hash values in the event data, and you suspect malware, you can optionally run a search for the file hash to see if the file is identified as malware by anti-malware tools. ", "version": 1 -} \ No newline at end of file +} diff --git a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_process_all_hosts.json b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_process_all_hosts.json index 1229c4a52b97d8..f9adfeb830618a 100644 --- a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_process_all_hosts.json +++ b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_process_all_hosts.json @@ -20,5 +20,6 @@ "Windows" ], "type": "machine_learning", + "note": "### Investigating an Unusual Windows Process ###\nSignals from this rule indicate the presence of a Windows process that is rare and unusual for all of the Windows hosts for which Winlogbeat data is available. Here are some possible avenues of investigation:\n- Consider the user as identified by the username field. Is this program part of an expected workflow for the user who ran this program on this host?\n- Examine the history of execution. If this process manifested only very recently, it might be part of a new software package. If it has a consistent cadence - for example if it runs monthly or quarterly - it might be part of a monthly or quarterly business process.\n- Examine the process metadata like the values of the Company, Description and Product fields which may indicate whether the program is associated with an expected software vendor or package. \n- Examine arguments and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.\n- Consider the same for the parent process. If the parent process is a legitimate system utility or service, this could be related to software updates or system management. If the parent process is something user-facing like an Office application, this process could be more suspicious.\n- If you have file hash values in the event data, and you suspect malware, you can optionally run a search for the file hash to see if the file is identified as malware by anti-malware tools. ", "version": 1 -} \ No newline at end of file +} diff --git a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_user_name.json b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_user_name.json index 703dc1a1dc6338..a0c6ff5c938f1c 100644 --- a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_user_name.json +++ b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_anomalous_user_name.json @@ -20,5 +20,6 @@ "Windows" ], "type": "machine_learning", + "note": "### Investigating an Unusual Windows User ###\nSignals from this rule indicate activity for a Windows user name that is rare and unusual. Here are some possible avenues of investigation:\n- Consider the user as identified by the username field. Is this program part of an expected workflow for the user who ran this program on this host? Could this be related to occasional troubleshooting or support activity?\n- Examine the history of user activity. If this user manifested only very recently, it might be a service account for a new software package. If it has a consistent cadence - for example if it runs monthly or quarterly - it might be part of a monthly or quarterly business process.\n- Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks that the user is performing.\n- Consider the same for the parent process. If the parent process is a legitimate system utility or service, this could be related to software updates or system management. If the parent process is something user-facing like an Office application, this process could be more suspicious.", "version": 1 -} \ No newline at end of file +} diff --git a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_rare_user_type10_remote_login.json b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_rare_user_type10_remote_login.json index 946cdb95b8e702..7318364c3aac27 100644 --- a/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_rare_user_type10_remote_login.json +++ b/x-pack/legacy/plugins/siem/server/lib/detection_engine/rules/prepackaged_rules/windows_rare_user_type10_remote_login.json @@ -20,5 +20,6 @@ "Windows" ], "type": "machine_learning", + "note": "### Investigating an Unusual Windows User ###\nSignals from this rule indicate activity for a rare and unusual Windows RDP (remote desktop) user. Here are some possible avenues of investigation:\n- Consider the user as identified by the username field. Is the user part of a group who normally logs into Windows hosts using RDP (remote desktop protocol)? Is this logon activity part of an expected workflow for the user? \n- Consider the source of the login. If the source is remote, could this be related to occasional troubleshooting or support activity by a vendor or an employee working remotely?", "version": 1 -} \ No newline at end of file +}