Detecting Lateral Movement With Zeek

Detecting Lateral Movement With Zeek

Overview

Cybersecurity skill for detecting lateral movement with zeek. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "detecting lateral movement with zeek"

• "Detect lateral movement in network traffic using Zeek (formerly Bro) log analysi"

• Hunting for lateral movement after an initial compromise indicator is found on one endpoint

• Investigating suspected NTLM account spray or Pass-the-Ticket attacks across the internal network

• Monitoring SMB traffic for unauthorized file transfers to admin shares (C$, ADMIN$, IPC$)

• Detecting remote service execution via DCE/RPC (PsExec, schtasks, WMI lateral patterns)

• Building alerting rules for internal network anomalies in a Zeek-based NSMP deployment

• Performing post-incident timeline reconstruction using Zeek logs as a network-level evidence source

Do not use as a standalone detection mechanism. Zeek sees network traffic only; combine with endpoint telemetry (Sysmon, EDR) for full visibility. Encrypted SMB3 traffic may limit Zeek's visibility into file-level details.

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

Prerequisites

• Zeek 6.0+ deployed on a network tap or SPAN port monitoring internal VLAN traffic
• Zeek SMB analyzer enabled (loaded by default: @load base/protocols/smb)
• Zeek DCE/RPC analyzer enabled (@load base/protocols/dce-rpc)
• Zeek Kerberos analyzer enabled (@load base/protocols/krb)
• Python 3.8+ (standard library only)
• Access to Zeek log directory (default: /opt/zeek/logs/current/)
• Familiarity with Zeek TSV log format (fields separated by \t, header lines prefixed with #)

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}

1. Define Detection Scope — Identify the specific lateral movement techniques or indicators to hunt. Map to MITRE ATT&CK tactics/techniques where applicable.
2. Collect Baseline Data — Gather historical logs and establish normal behavior patterns for lateral movement.
3. Build Detection Queries — Write zeek queries targeting lateral movement indicators. Use platform-specific query language for optimal performance.
4. Execute Hunts — Run queries against the collected data, starting with broad filters and narrowing down.
5. Triage Results — Investigate alerts, filter false positives, and validate findings against known-good behavior.
6. Document Findings — Record confirmed detections, IOCs, and affected systems. Update detection rules based on findings.

Tools

• zeek — Primary tool for this skill
• SIEM Platform — Central log aggregation and query execution
• Sigma Rules — Vendor-agnostic detection rule format
• MITRE ATT&CK Navigator — Technique mapping and coverage analysis

Process

1. Reconnaissance — Gather target information, identify attack surface, enumerate services
2. Analysis/Exploitation — Execute the technique, analyze results, document findings
3. Reporting — Document IOCs, write findings, provide remediation recommendations

Verification

• All lateral movement procedures executed completely and documented
• Findings validated against multiple data sources
• False positives identified and filtered
• Results documented with evidence and timestamps
• Recommendations provided with risk-based prioritization

Anti-Rationalization Table