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https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
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mukul975
141 lines
4.6 KiB
Python
141 lines
4.6 KiB
Python
#!/usr/bin/env python3
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"""
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BAS Results Analyzer and Control Effectiveness Calculator
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Processes Breach and Attack Simulation results to calculate
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security control effectiveness scores and identify gaps.
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Requirements:
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pip install pandas
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Usage:
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python process.py analyze --csv bas_results.csv --output control_scores.csv
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python process.py gaps --csv bas_results.csv --output gaps.csv
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python process.py trend --dir results/ --output trend.csv
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"""
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import argparse
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import sys
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from collections import defaultdict
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from datetime import datetime
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from pathlib import Path
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import pandas as pd
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CONTROL_TECHNIQUE_MAP = {
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"email_gateway": ["T1566.001", "T1566.002", "T1566.003"],
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"edr": ["T1059.001", "T1059.003", "T1003.001", "T1055", "T1547.001",
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"T1053.005", "T1027", "T1140"],
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"ngfw_proxy": ["T1071.001", "T1071.004", "T1048.001", "T1048.003",
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"T1572", "T1090"],
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"siem": ["T1087", "T1018", "T1069", "T1021.002", "T1021.001"],
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"dlp": ["T1048", "T1567", "T1041", "T1560"],
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"ndr": ["T1071", "T1021", "T1040", "T1046"],
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"waf": ["T1190", "T1210"],
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"pam": ["T1078", "T1134", "T1098"],
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}
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def analyze_control_effectiveness(df):
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"""Calculate control effectiveness from BAS results."""
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scores = []
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for control, techniques in CONTROL_TECHNIQUE_MAP.items():
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relevant = df[df["technique_id"].isin(techniques)]
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if len(relevant) == 0:
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continue
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total = len(relevant)
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prevented = len(relevant[relevant["result"] == "prevented"])
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detected = len(relevant[relevant["result"] == "detected"])
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missed = len(relevant[relevant["result"] == "missed"])
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scores.append({
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"control": control,
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"total_tests": total,
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"prevented": prevented,
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"detected": detected,
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"missed": missed,
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"prevention_rate": round(prevented / total * 100, 1),
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"detection_rate": round(detected / total * 100, 1),
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"effectiveness": round((prevented + detected) / total * 100, 1),
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"gap_rate": round(missed / total * 100, 1),
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})
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return pd.DataFrame(scores).sort_values("effectiveness", ascending=True)
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def identify_gaps(df):
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"""Identify attack techniques that bypass all controls."""
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missed = df[df["result"] == "missed"].copy()
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if len(missed) == 0:
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print("[+] No gaps found - all attacks were prevented or detected!")
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return pd.DataFrame()
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gaps = missed.groupby(["technique_id", "technique_name"]).agg(
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miss_count=("result", "count"),
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targets=("target", lambda x: ", ".join(x.unique())),
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).reset_index().sort_values("miss_count", ascending=False)
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return gaps
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def print_summary(scores_df, gaps_df):
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"""Print analysis summary."""
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print(f"\n{'=' * 70}")
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print("BAS CONTROL EFFECTIVENESS REPORT")
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print(f"{'=' * 70}")
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print(f"\nControl Scores:")
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for _, row in scores_df.iterrows():
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status = "PASS" if row["effectiveness"] >= 80 else "WARN" if row["effectiveness"] >= 60 else "FAIL"
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print(f" [{status}] {row['control']:<15} "
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f"Effectiveness: {row['effectiveness']}% "
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f"(Prevent: {row['prevention_rate']}% | "
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f"Detect: {row['detection_rate']}% | "
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f"Miss: {row['gap_rate']}%)")
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if len(gaps_df) > 0:
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print(f"\nTop Security Gaps (attacks that bypass controls):")
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for _, row in gaps_df.head(10).iterrows():
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print(f" {row['technique_id']}: {row['technique_name']} "
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f"({row['miss_count']} misses)")
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def main():
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parser = argparse.ArgumentParser(description="BAS Results Analyzer")
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subparsers = parser.add_subparsers(dest="command")
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a_p = subparsers.add_parser("analyze", help="Analyze control effectiveness")
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a_p.add_argument("--csv", required=True)
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a_p.add_argument("--output", default="control_scores.csv")
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g_p = subparsers.add_parser("gaps", help="Identify security gaps")
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g_p.add_argument("--csv", required=True)
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g_p.add_argument("--output", default="gaps.csv")
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args = parser.parse_args()
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if not args.command:
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parser.print_help()
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sys.exit(1)
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df = pd.read_csv(args.csv)
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if args.command == "analyze":
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scores = analyze_control_effectiveness(df)
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gaps = identify_gaps(df)
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print_summary(scores, gaps)
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scores.to_csv(args.output, index=False)
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print(f"\n[+] Control scores saved to {args.output}")
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elif args.command == "gaps":
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gaps = identify_gaps(df)
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gaps.to_csv(args.output, index=False)
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print(f"[+] {len(gaps)} gaps saved to {args.output}")
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if __name__ == "__main__":
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main()
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