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name: detecting-service-account-abuse description: Detect abuse of service accounts by hunting for anomalous interactive logons, privilege escalation, and lateral movement using EDR/SIEM telemetry (CrowdStrike Falcon, Microsoft Defender, Splunk, Elastic Security, Sysmon, Velociraptor) and Sigma detection rules. Use when hunting for service-account misuse or investigating a service account performing unexpected interactive logons. 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 d3fend_techniques:
• Restore Access
• Password Authentication
• Biometric Authentication
• Strong Password Policy
• Restore User Account Access nist_csf:
• DE.CM-01
• DE.AE-02
• DE.AE-07
• ID.RA-05 mitre_attack:
• T1078.002
• T1021.001
• T1098.001
• T1550.002

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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]