Examine Zeek connection logs for regular callbacks that may indicate communication with an attacker’s control server. Use ZAT and Pandas to group connections, measure timing variation, and identify source and destination pairs for further investigation.
Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.
Review the instructions the agent will receive before you install.
Load Zeek conn.log data using ZAT (Zeek Analysis Tools), group connections by source/destination pairs, and compute timing statistics to identify beaconing.
from zat.log_to_dataframe import LogToDataFrame
import numpy as np
log_to_df = LogToDataFrame()
conn_df = log_to_df.create_dataframe('/path/to/conn.log')
# Group by src/dst pair and calculate inter-arrival time
for (src, dst), group in conn_df.groupby(['id.orig_h', 'id.resp_h']):
times = group['ts'].sort_values()
intervals = times.diff().dt.total_seconds().dropna()
if len(intervals) > 10:
std_dev = np.std(intervals)
mean_interval = np.mean(intervals)
# Low std_dev relative to mean = likely beaconing
Key analysis steps:
from zat.log_to_dataframe import LogToDataFrame
log_to_df = LogToDataFrame()
df = log_to_df.create_dataframe('conn.log')
print(df[['id.orig_h', 'id.resp_h', 'ts', 'duration']].head())
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