detecting-service-account-abuse
$
npx mdskill add mukul975/Anthropic-Cybersecurity-Skills/detecting-service-account-abuseDetect service account abuse via anomalous logins and privilege escalation.
- Identifies suspicious patterns like interactive logins and unauthorized access.
- Integrates with EDR, SIEM, Sysmon, and Windows Security Event Logs.
- Correlates threat intelligence feeds to validate detection hypotheses.
- Delivers actionable alerts for incident response and threat hunting.
SKILL.md
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--- name: detecting-service-account-abuse description: Detect abuse of service accounts through anomalous interactive logons, privilege escalation, lateral movement, and unauthorized access patterns. domain: cybersecurity subdomain: threat-hunting tags: [threat-hunting, mitre-attack, service-accounts, privilege-escalation, t1078, proactive-detection] version: "1.0" author: mahipal license: Apache-2.0 --- # Detecting Service Account Abuse ## When to Use - When proactively hunting for indicators of detecting service account abuse in the environment - After threat intelligence indicates active campaigns using these techniques - During incident response to scope compromise related to these techniques - When EDR or SIEM alerts trigger on related indicators - During periodic security assessments and purple team exercises ## Prerequisites - EDR platform with process and network telemetry (CrowdStrike, MDE, SentinelOne) - SIEM with relevant log data ingested (Splunk, Elastic, Sentinel) - Sysmon deployed with comprehensive configuration - Windows Security Event Log forwarding enabled - Threat intelligence feeds for IOC correlation ## Workflow 1. **Formulate Hypothesis**: Define a testable hypothesis based on threat intelligence or ATT&CK gap analysis. 2. **Identify Data Sources**: Determine which logs and telemetry are needed to validate or refute the hypothesis. 3. **Execute Queries**: Run detection queries against SIEM and EDR platforms to collect relevant events. 4. **Analyze Results**: Examine query results for anomalies, correlating across multiple data sources. 5. **Validate Findings**: Distinguish true positives from false positives through contextual analysis. 6. **Correlate Activity**: Link findings to broader attack chains and threat actor TTPs. 7. **Document and Report**: Record findings, update detection rules, and recommend response actions. ## Key Concepts | Concept | Description | |---------|-------------| | T1078.002 | Domain Accounts | | T1078.001 | Default Accounts | | T1021 | Remote Services | ## Tools & Systems | Tool | Purpose | |------|---------| | CrowdStrike Falcon | EDR telemetry and threat detection | | Microsoft Defender for Endpoint | Advanced hunting with KQL | | Splunk Enterprise | SIEM log analysis with SPL queries | | Elastic Security | Detection rules and investigation timeline | | Sysmon | Detailed Windows event monitoring | | Velociraptor | Endpoint artifact collection and hunting | | Sigma Rules | Cross-platform detection rule format | ## Common Scenarios 1. **Scenario 1**: Service account RDP to domain controller 2. **Scenario 2**: SQL service accessing file shares outside scope 3. **Scenario 3**: Backup service lateral movement off-hours 4. **Scenario 4**: Compromised svc with DA privileges used for DCSync ## Output Format ``` Hunt ID: TH-DETECT-[DATE]-[SEQ] Technique: T1078.002 Host: [Hostname] User: [Account context] Evidence: [Log entries, process trees, network data] Risk Level: [Critical/High/Medium/Low] Confidence: [High/Medium/Low] Recommended Action: [Containment, investigation, monitoring] ```
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