A security analyst is investigating a suspected beaconing behavior in network traffic logs. The traffic consistently shows small, periodic connections to an external IP. Which statistical analysis method is most effective for identifying this pattern within high-volume PCAP or NetFlow data?
Beaconing produces regular callbacks, so measuring the intervals between connections and looking for low variance or a dominant periodicity exposes the pattern. Frequency analysis of connection time intervals isolates that rhythm within high-volume PCAP or NetFlow data.
Why this answer
Beaconing is characterized by regular, periodic callbacks (e.g., every 60 seconds), so analyzing the distribution of inter-connection time intervals reveals the low-variance periodicity that distinguishes C2 traffic from normal user-driven traffic. Frequency analysis (e.g., via histograms, Fourier transforms, or standard deviation of intervals) surfaces this pattern even in high-volume PCAP/NetFlow data. Bandwidth, port entropy, and byte counts do not capture the temporal regularity that defines beaconing.
Exam trap
The trap here is confusing volumetric/aggregate statistics (top talkers, mean bytes) with temporal pattern analysis — candidates pick bandwidth-based options because they sound like 'traffic analysis' but miss that beaconing is defined by timing regularity, not volume.
How to eliminate wrong answers
Option B is wrong because top-talkers analysis ranks hosts by total volume, and beaconing traffic is deliberately low-volume and stealthy, so it will never appear in a top-talkers list. Option C is wrong because source port entropy measures randomness in port selection, which is unrelated to the periodic timing pattern of beaconing and is more relevant to detecting port-scan or randomization behavior. Option D is wrong because mean bytes per session is a volumetric statistic that ignores timing entirely; beaconing sessions are typically uniform in size, so the mean alone cannot distinguish them from other small flows.