Bobby Filar
9a739b7e4c
* modifying rules assoc w/ deprecation of v2 ML jobs * modified updated_date field * fixed machine_learning_job_id and added min_stack_version * replacing rest of deprecated jobs with new naming convention * Update ml_suspicious_login_activity.toml * removing rules assoc w/ deprecated ML jobs * Update rules/ml/ml_linux_anomalous_compiler_activity.toml Co-authored-by: Justin Ibarra <brokensound77@users.noreply.github.com> * Update rules/ml/ml_linux_anomalous_compiler_activity.toml Co-authored-by: Justin Ibarra <brokensound77@users.noreply.github.com> * updated ml job rules to reflect 8.3 changes * updating min_stack_version for ml detection rules Co-authored-by: Craig Chamberlain <randomuserid@users.noreply.github.com> Co-authored-by: Justin Ibarra <brokensound77@users.noreply.github.com> Co-authored-by: Apoorva Joshi <30438249+ajosh0504@users.noreply.github.com>
39 lines
2.3 KiB
TOML
39 lines
2.3 KiB
TOML
[metadata]
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creation_date = "2020/03/25"
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maturity = "production"
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updated_date = "2022/05/12"
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min_stack_comments = "Supports latest version of ML job introduced in 8.3"
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min_stack_version = "8.3.0"
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[rule]
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anomaly_threshold = 50
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author = ["Elastic"]
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description = """
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Identifies Linux processes that do not usually use the network but have unexpected network activity, which can indicate
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command-and-control, lateral movement, persistence, or data exfiltration activity. A process with unusual network
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activity can denote process exploitation or injection, where the process is used to run persistence mechanisms that
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allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized network
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applications.
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"""
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from = "now-45m"
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interval = "15m"
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license = "Elastic License v2"
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machine_learning_job_id = ["v3_linux_anomalous_network_activity"]
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name = "Unusual Linux Network Activity"
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note = """## Triage and analysis
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### Investigating Unusual Network Activity
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Detection alerts 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:
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- Consider the IP addresses and ports. Are these used by normal but infrequent network workflows? Are they expected or unexpected?
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- 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.
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- 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?
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- Examine the history of execution. If this process only manifested 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.
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- 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."""
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references = ["https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html"]
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risk_score = 21
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rule_id = "52afbdc5-db15-485e-bc24-f5707f820c4b"
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severity = "low"
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tags = ["Elastic", "Host", "Linux", "Threat Detection", "ML"]
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type = "machine_learning"
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