--- name: hunting-for-beaconing-with-frequency-analysis-79c3e65d9d10 description: Identify command-and-control beaconing patterns in network traffic by applying statistical frequency analysis, jitter calculation, and coefficient of variation scoring to detect periodic callbacks from compromised endpoints. domain: cybersecurity subdomain: threat-hunting tags: - threat-hunting - beaconing - c2-detection - frequency-analysis - network-traffic - RITA - jitter-detection - mitre-t1071 version: '1.0' author: mahipal license: Apache-2.0 d3fend_techniques: - File Metadata Consistency Validation - Certificate Analysis - Application Protocol Command Analysis - Content Format Conversion - File Content Analysis nist_csf: - DE.CM-01 - DE.AE-02 - DE.AE-07 - ID.RA-05 mitre_attack: - T1046 - T1057 - T1082 - T1083 - T1071 --- # Hunting for Beaconing with Frequency Analysis ## When to Use - When proactively searching for compromised endpoints calling back to C2 infrastructure - After threat intelligence reports indicate active C2 frameworks targeting your sector - When network logs show periodic outbound connections to unfamiliar destinations - During purple team exercises validating C2 detection capabilities - When investigating a potential breach and need to identify active C2 channels ## Prerequisites - Network proxy/firewall logs with timestamps and destination data (minimum 24 hours) - Zeek conn.log, dns.log, and ssl.log or equivalent NetFlow/IPFIX data - SIEM platform with statistical analysis capability (Splunk, Elastic, Microsoft Sentinel) - RITA (Real Intelligence Threat Analytics) or AC-Hunter for automated beacon analysis - Threat intelligence feeds for domain/IP reputation enrichment ## Workflow 1. **Define Beacon Parameters**: Establish detection thresholds -- coefficient of variation (CV) below 0.20 indicates strong periodicity, minimum 50 connections over 24 hours, average interval between 30 seconds and 24 hours. 2. **Collect Network Telemetry**: Aggregate proxy logs, DNS queries, firewall connection logs, and Zeek metadata into the analysis platform. 3. **Calculate Connection Intervals**: For each source-destination pair, compute the time delta between consecutive connections and derive mean interval, standard deviation, and CV. 4. **Apply Jitter Analysis**: Sophisticated C2 frameworks like Cobalt Strike add jitter (randomness) to beacon intervals. The Sunburst backdoor beaconed every 15 minutes plus/minus 90 seconds. Analyze jitter patterns to detect even randomized beaconing. 5. **Filter Legitimate Periodic Traffic**: Exclude known-good beaconing sources including Windows Update, antivirus definition updates, NTP synchronization, SaaS heartbeat services, and CDN health checks. 6. **Analyze Data Size Consistency**: C2 heartbeat packets typically have consistent payload sizes. Calculate the CV of bytes transferred per connection -- low variance suggests automated communication. 7. **Enrich with Threat Intelligence**: Check identified beaconing destinations against VirusTotal, WHOIS registration data (flag domains under 30 days old), certificate transparency logs, and passive DNS history. 8. **Correlate with Endpoint Telemetry**: Map beaconing source IPs to endpoint hostnames via DHCP logs, then correlate with process creation events (Sysmon Event ID 1, 3) to identify the responsible process. 9. **Score and Prioritize**: Assign risk scores based on CV value, domain age, TI matches, data size consistency, and suspicious port usage. Escalate high-confidence findings. ## Key Concepts | Concept | Description | |---------|-------------| | T1071.001 | Application Layer Protocol: Web Protocols -- HTTP/HTTPS beaconing | | T1071.004 | Application Layer Protocol: DNS -- DNS-based C2 tunneling | | T1573 | Encrypted Channel -- TLS/SSL encrypted C2 communication | | T1568.002 | Dynamic Resolution: Domain Generation Algorithms | | Coefficient of Variation | Standard deviation divided by mean; values below 0.20 indicate periodicity | | Jitter | Random variation added to beacon interval to evade detection | | RITA Beacon Score | Composite score from connection regularity, data size consistency, and connection count | | JA3/JA4 Fingerprinting | TLS client fingerprinting to identify C2 framework signatures | | Fast-Flux DNS | Rapidly changing DNS resolution used to protect C2 infrastructure | ## Tools & Systems | Tool | Purpose | |------|---------| | RITA (Real Intelligence Threat Analytics) | Automated beacon scoring from Zeek logs | | AC-Hunter | Commercial threat hunting platform with beacon detection | | Splunk | SPL-based statistical beacon analysis with streamstats | | Elastic Security | ML anomaly detection for periodic network behavior | | Zeek | Network metadata collection (conn.log, dns.log, ssl.log) | | Suricata | Network IDS with JA3/JA4 TLS fingerprint extraction | | FLARE | C2 profile and beacon pattern detection | | VirusTotal | Domain and IP reputation enrichment | ## Detection Queries ### Splunk -- HTTP/S Beacon Frequency Analysis ```spl index=proxy OR index=firewall | where NOT match(dest, "(?i)(microsoft|google|amazonaws|cloudflare|akamai)") | bin _time span=1s | stats count by src_ip dest _time | streamstats current=f last(_time) as prev_time by src_ip dest | eval interval=_time-prev_time | stats count avg(interval) as avg_interval stdev(interval) as stdev_interval min(interval) as min_interval max(interval) as max_interval by src_ip dest | where count > 50 | eval cv=stdev_interval/avg_interval | where cv < 0.20 AND avg_interval > 30 AND avg_interval < 86400 | sort cv | table src_ip dest count avg_interval stdev_interval cv ``` ### KQL -- Microsoft Sentinel Beacon Detection ```kql DeviceNetworkEvents | where Timestamp > ago(24h) | where RemoteIPType == "Public" | summarize ConnectionTimes=make_list(Timestamp), Count=count() by DeviceName, RemoteIP, RemoteUrl | where Count > 50 | extend Intervals = array_sort_asc(ConnectionTimes) | mv-apply Intervals on ( extend NextTime = next(Intervals) | where isnotempty(NextTime) | extend IntervalSec = datetime_diff('second', NextTime, Intervals) | summarize AvgInterval=avg(IntervalSec), StdDev=stdev(IntervalSec) ) | extend CV = StdDev / AvgInterval | where CV < 0.2 and AvgInterval > 30 | sort by CV asc ``` ### Sigma Rule -- Beaconing Pattern Detection ```yaml title: Potential C2 Beaconing Pattern Detected status: experimental logsource: category: proxy detection: selection: dst_ip|cidr: '!10.0.0.0/8' timeframe: 24h condition: selection | count(dst) by src_ip > 50 level: medium tags: - attack.command_and_control - attack.t1071.001 ``` ## Common Scenarios 1. **Cobalt Strike Beacon**: Default 60-second interval with configurable 0-50% jitter over HTTPS. Malleable C2 profiles can mimic legitimate traffic patterns. 2. **Sunburst/SUNSPOT**: 12-14 day dormancy period, then beaconing every 12-14 minutes with randomized jitter, designed to evade frequency analysis. 3. **DNS Tunneling C2**: Encoded data exfiltration via DNS TXT/CNAME queries to attacker-controlled domains, detectable via high subdomain entropy and query volume. 4. **Sliver C2**: Modern C2 framework with HTTPS, mTLS, and WireGuard protocols, configurable beacon intervals with built-in jitter support. 5. **Legitimate Service Abuse**: C2 communication over Slack, Discord, Telegram, or cloud storage APIs, making destination-based filtering ineffective. ## Output Format ``` Hunt ID: TH-BEACON-[DATE]-[SEQ] Source IP: [Internal IP] Source Host: [Hostname from DHCP/DNS] Destination: [Domain/IP] Protocol: [HTTP/HTTPS/DNS] Beacon Interval: [Average seconds] Jitter Estimate: [Percentage] Coefficient of Variation: [CV value] Connection Count: [Total connections in window] Data Size CV: [Payload consistency metric] Domain Age: [Days since registration] TI Match: [Yes/No -- source] Risk Score: [0-100] Risk Level: [Critical/High/Medium/Low] Indicators: [List of triggered risk factors] ``` --- Source attribution: 26zl Original project: https://github.com/26zl/cybersec-toolkit/blob/2ad2519d437cda1f8a822ef7adc336993601a5ac/.claude/skills/hunting-for-beaconing-with-frequency-analysis/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: 26zl Original project: https://github.com/26zl/cybersec-toolkit/blob/2ad2519d437cda1f8a822ef7adc336993601a5ac/.claude/skills/hunting-for-beaconing-with-frequency-analysis/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering Roles: Security Engineer Specialties: API Tools: Rita, Siem Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: b5182be0555e7c008f63e6ab5a25ee6b1213ff6d69e75e48c2e59e8b4ae01062