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https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
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mukul975
56 lines
2.2 KiB
Python
56 lines
2.2 KiB
Python
#!/usr/bin/env python3
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"""Fileless Attack Detector - Scans PowerShell logs for fileless attack indicators."""
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import json, csv, re, sys, os
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from collections import Counter
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from datetime import datetime
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FILELESS_PATTERNS = {
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"encoded_command": r"(?i)(-enc\s|-e\s|-encodedcommand|frombase64string)",
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"download_cradle": r"(?i)(downloadstring|invoke-webrequest|net\.webclient|wget\s|curl\s)",
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"amsi_bypass": r"(?i)(amsiutils|amsiinitfailed|amsi\.dll)",
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"reflection": r"(?i)(system\.reflection|loadassembly|gettype.*invoke)",
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"wmi_abuse": r"(?i)(win32_process.*create|wmiclass|managementclass)",
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"credential_access": r"(?i)(mimikatz|invoke-mimikatz|sekurlsa|logonpasswords)",
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"invoke_expression": r"(?i)(iex\s|invoke-expression)",
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}
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def scan_powershell_logs(csv_path: str) -> list:
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detections = []
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with open(csv_path, "r", encoding="utf-8-sig") as f:
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for row in csv.DictReader(f):
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script = row.get("ScriptBlockText", row.get("Message", ""))
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for pattern_name, pattern in FILELESS_PATTERNS.items():
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if re.search(pattern, script):
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detections.append({
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"timestamp": row.get("TimeCreated", row.get("Date and Time", "")),
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"host": row.get("Computer", row.get("MachineName", "")),
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"technique": pattern_name,
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"script_excerpt": script[:300],
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})
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break
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return detections
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def generate_report(detections: list, output_path: str) -> None:
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by_technique = Counter(d["technique"] for d in detections)
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report = {
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"report_generated": datetime.utcnow().isoformat() + "Z",
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"total_detections": len(detections),
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"by_technique": dict(by_technique),
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"detections": detections[:100],
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}
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with open(output_path, "w") as f:
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json.dump(report, f, indent=2)
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if __name__ == "__main__":
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if len(sys.argv) < 2:
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print("Usage: python process.py <powershell_logs.csv>")
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sys.exit(1)
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detections = scan_powershell_logs(sys.argv[1])
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out = os.path.join(os.path.dirname(sys.argv[1]) or ".", "fileless_detection_report.json")
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generate_report(detections, out)
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print(f"Detections: {len(detections)}")
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