From cybersecurity-skills
Detects C2 beaconing patterns in Zeek conn.log by analyzing connection inter-arrival times with ZAT, Pandas, and low jitter thresholds. For threat hunting callbacks.
npx claudepluginhub mukul975/anthropic-cybersecurity-skills --plugin cybersecurity-skillsThis skill uses the workspace's default tool permissions.
- When investigating security incidents that require detecting beaconing patterns with zeek
Applies Acme Corporation brand guidelines including colors, fonts, layouts, and messaging to generated PowerPoint, Excel, and PDF documents.
Builds DCF models with sensitivity analysis, Monte Carlo simulations, and scenario planning for investment valuation and risk assessment.
Calculates profitability (ROE, margins), liquidity (current ratio), leverage, efficiency, and valuation (P/E, EV/EBITDA) ratios from financial statements in CSV, JSON, text, or Excel for investment analysis.
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())