Skill UI

Examines packet captures, Zeek logs, and NetFlow data to uncover C2 channels, lateral movement, and exfiltration during security incidents, guiding analysts through capture, filtering, and IOC extraction workflows.

A comprehensive guide for analyzing PCAP files captured during malware execution. It demonstrates advanced techniques using Wireshark, Zeek, Suricata, and Python (scapy) to identify Command and Control (C2) communications, data exfiltration channels, Domain Generation Algorithms (DGA), and periodic beaconing patterns. Essential for malware reverse engineering and network threat detection.

This comprehensive skill analyzes Zeek conn.log and NetFlow data to proactively detect advanced indicators of ransomware activity. It identifies critical threats such as Command and Control (C2) beaconing patterns, connections to TOR exit nodes, large-scale data exfiltration flows, and suspicious DNS anomalies. It is essential for threat hunting and complex security incident response.

This script performs statistical analysis on Zeek connection logs (conn.log) to detect Command and Control (C2) beaconing patterns. It leverages the ZAT library to load data into Pandas DataFrames, calculates inter-arrival time statistics, and flags connections with low standard deviation relative to the mean. This technique is crucial for network threat hunting and security incident investigation when identifying malicious, periodic callbacks.

This skill provides a methodology to detect data exfiltration attempts hidden within DNS queries. By analyzing Zeek dns.log files, the agent computes Shannon entropy of subdomains, flags unusually long labels, and monitors unique subdomain counts per parent domain. This technique is crucial for identifying malicious tunneling tools like dnscat2 or iodine that bypass traditional security controls.

A comprehensive guide and framework for detecting attacker lateral movement within enterprise networks. It details the use of network flow analysis (Zeek), authentication log review (Windows Event Logs, Kerberos), and dedicated SIEM correlation rules (Splunk, Elastic) to identify techniques like Pass-the-Hash and RDP hopping. Ideal for threat hunters and security engineers building detection pipelines.

This skill provides comprehensive anomaly detection for Modbus/TCP and Modbus RTU traffic in Operational Technology (OT) and Industrial Control Systems (ICS). It monitors function codes, validates register access, analyzes timing, and detects unauthorized clients. By utilizing advanced tools like Zeek, Suricata, and custom Python models, it helps establish behavioral baselines and identify potential cyber threats.

This skill provides a comprehensive guide to deploying Zeek (formerly Bro) for passive network security monitoring. It details how to generate structured logs (DNS, HTTP, SSL, etc.) and write custom Zeek scripts to detect sophisticated anomalies like DNS tunneling and beaconing. Ideal for threat hunting, security incident response, and complementing traditional IDS solutions.

A comprehensive skill for detecting early-stage ransomware indicators before data encryption occurs. It utilizes advanced network detection tools such as Zeek and Suricata, combined with SIEM correlation rules and threat intelligence feeds. This method monitors the entire pre-encryption kill chain, identifying precursor behaviors like initial access broker activity, Cobalt Strike C2 beaconing, credential harvesting (e.g., Mimikatz signatures), and data staging attempts, enabling timely incident response and containment.

This skill provides a systematic methodology for detecting Command and Control (C2) beaconing patterns in network traffic. It utilizes advanced statistical techniques, including Coefficient of Variation (CV) and jitter analysis, applied to network flow logs (Zeek, proxy, firewall). It is crucial for proactive threat hunting and incident response to identify compromised endpoints communicating periodically with malicious infrastructure.

This skill guides threat hunters in detecting Cobalt Strike Command and Control (C2) beacons. It utilizes advanced network forensic techniques, including analyzing default TLS certificate serials, JA3/JA3S/JARM fingerprints, beacon jitter, and HTTP profile matching. Detection is achieved by correlating data from Zeek logs, Suricata rules, and Python PCAP analysis to identify suspicious outbound communications.

A comprehensive guide and detection methodology for identifying covert communication channels utilizing DNS records. This technique analyzes Zeek dns.log for indicators such as high-entropy subdomains, excessive query volume, unusually long query lengths, and abnormal record type distributions, which suggest data exfiltration or Command and Control (C2) activity.