detecting-beaconing-patterns-with-zeek

Beaconing detection involves analyzing Zeek conn.log inter-arrival times to identify suspicious periodicity and low-variance patterns indicative of C2 activity. This statistical approach flags regular communication intervals between compromised hosts and command and control servers.

You can analyze Zeek logs for beaconing by loading conn.log data with the ZAT library and using pandas to compute interval statistics. This process groups connections by source, destination, and port to identify low-variance inter-arrival times characteristic of beaconing behavior.

Yes, you need the ZAT library along with Python 3.8+, numpy, and pandas to load Zeek logs and compute beaconing metrics. These dependencies are required to parse conn.log files and perform the statistical analysis needed for beaconing detection.

Yes, you can use this beaconing analysis for incident response, threat hunting, and network forensics. By applying statistical analysis to Zeek conn.log and dns.log data, it reveals C2 beaconing behavior and suspicious periodicity for faster detection during investigations.

The best way to identify low-variance beacon patterns is to statistically analyze inter-arrival times of network connections. By computing interval statistics on Zeek conn.log data grouped by source, destination, and port, you can reveal regular periodicity indicative of C2 beaconing.

Yes, this beaconing detection approach supports grouping by source, destination, and port to identify beaconing between hosts. This grouping allows the statistical analysis to isolate specific communication flows in Zeek conn.log data and accurately flag suspicious periodic connections.