Analyzing Dns Logs For Exfiltration

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#!/usr/bin/env python3
"""DNS exfiltration detection agent using entropy analysis and query pattern detection."""

import math
from collections import Counter, defaultdict


def shannon_entropy(text):
    """Calculate Shannon entropy of a string."""
    if not text:
        return 0.0
    counter = Counter(text.lower())
    length = len(text)
    entropy = -sum(
        (count / length) * math.log2(count / length)
        for count in counter.values()
    )
    return round(entropy, 4)


def extract_subdomain(fqdn):
    """Extract the subdomain portion from a fully qualified domain name."""
    parts = fqdn.rstrip(".").split(".")
    if len(parts) > 2:
        return ".".join(parts[:-2])
    return ""


def extract_registered_domain(fqdn):
    """Extract the registered domain (SLD + TLD) from an FQDN."""
    parts = fqdn.rstrip(".").split(".")
    if len(parts) >= 2:
        return ".".join(parts[-2:])
    return fqdn


def detect_tunneling(dns_records, subdomain_len_threshold=50, min_queries=20):
    """Detect DNS tunneling based on subdomain length anomalies."""
    domain_stats = defaultdict(lambda: {"queries": 0, "unique_queries": set(),
                                         "subdomain_lengths": [], "sources": set()})
    for record in dns_records:
        query = record.get("query", "")
        src = record.get("src_ip", "unknown")
        subdomain = extract_subdomain(query)
        reg_domain = extract_registered_domain(query)
        if len(subdomain) > subdomain_len_threshold:
            stats = domain_stats[reg_domain]
            stats["queries"] += 1
            stats["unique_queries"].add(query)
            stats["subdomain_lengths"].append(len(subdomain))
            stats["sources"].add(src)
    alerts = []
    for domain, stats in domain_stats.items():
        if stats["queries"] >= min_queries:
            avg_len = sum(stats["subdomain_lengths"]) / len(stats["subdomain_lengths"])
            max_len = max(stats["subdomain_lengths"])
            alerts.append({
                "domain": domain,
                "queries": stats["queries"],
                "unique_queries": len(stats["unique_queries"]),
                "avg_subdomain_length": round(avg_len, 1),
                "max_subdomain_length": max_len,
                "sources": list(stats["sources"]),
                "verdict": "CRITICAL - Likely DNS tunneling",
            })
    return sorted(alerts, key=lambda x: x["avg_subdomain_length"], reverse=True)


def detect_dga(dns_records, entropy_threshold=3.5, min_sld_length=12):
    """Detect Domain Generation Algorithm queries using entropy scoring."""
    suspicious = defaultdict(lambda: {"count": 0, "sources": set(), "entropies": []})
    for record in dns_records:
        query = record.get("query", "").rstrip(".")
        src = record.get("src_ip", "unknown")
        parts = query.split(".")
        if len(parts) < 2:
            continue
        sld = parts[-2]
        if len(sld) < min_sld_length:
            continue
        ent = shannon_entropy(sld)
        if ent > entropy_threshold:
            suspicious[query]["count"] += 1
            suspicious[query]["sources"].add(src)
            suspicious[query]["entropies"].append(ent)
    alerts = []
    for domain, data in suspicious.items():
        avg_entropy = sum(data["entropies"]) / len(data["entropies"])
        alerts.append({
            "domain": domain,
            "queries": data["count"],
            "avg_entropy": round(avg_entropy, 4),
            "sources": list(data["sources"]),
            "verdict": "HIGH - Possible DGA domain",
        })
    return sorted(alerts, key=lambda x: x["avg_entropy"], reverse=True)


def detect_volume_anomaly(dns_records, z_score_threshold=3.0):
    """Detect hosts with anomalously high DNS query volumes."""
    host_counts = defaultdict(int)
    for record in dns_records:
        src = record.get("src_ip", "unknown")
        host_counts[src] += 1
    if not host_counts:
        return []
    values = list(host_counts.values())
    mean_q = sum(values) / len(values)
    if len(values) < 2:
        return []
    variance = sum((x - mean_q) ** 2 for x in values) / (len(values) - 1)
    stdev_q = variance ** 0.5
    if stdev_q == 0:
        return []
    anomalies = []
    for host, count in host_counts.items():
        z = (count - mean_q) / stdev_q
        if z > z_score_threshold:
            anomalies.append({
                "src_ip": host,
                "queries": count,
                "z_score": round(z, 2),
                "mean": round(mean_q, 1),
                "verdict": "HIGH - Anomalous query volume",
            })
    return sorted(anomalies, key=lambda x: x["z_score"], reverse=True)


def detect_txt_abuse(dns_records, threshold=100):
    """Detect excessive TXT record queries (common tunneling method)."""
    txt_counts = defaultdict(lambda: {"count": 0, "unique_domains": set()})
    for record in dns_records:
        qtype = str(record.get("query_type", "")).upper()
        if qtype in ("TXT", "16"):
            src = record.get("src_ip", "unknown")
            txt_counts[src]["count"] += 1
            txt_counts[src]["unique_domains"].add(record.get("query", ""))
    alerts = []
    for src, data in txt_counts.items():
        if data["count"] > threshold:
            level = "CRITICAL" if data["count"] > 1000 else "HIGH" if data["count"] > 500 else "MEDIUM"
            alerts.append({
                "src_ip": src,
                "txt_queries": data["count"],
                "unique_domains": len(data["unique_domains"]),
                "verdict": f"{level} - Possible DNS tunneling via TXT records",
            })
    return sorted(alerts, key=lambda x: x["txt_queries"], reverse=True)


def estimate_exfil_volume(dns_records, target_domain):
    """Estimate data volume encoded in DNS queries to a specific domain."""
    total_encoded_bytes = 0
    query_count = 0
    for record in dns_records:
        query = record.get("query", "")
        if target_domain in query:
            subdomain = extract_subdomain(query)
            total_encoded_bytes += len(subdomain)
            query_count += 1
    decoded_bytes = int(total_encoded_bytes * 0.75)  # Base64 decode factor
    return {
        "target_domain": target_domain,
        "total_queries": query_count,
        "encoded_bytes": total_encoded_bytes,
        "estimated_decoded_bytes": decoded_bytes,
        "estimated_kb": round(decoded_bytes / 1024, 1),
        "estimated_mb": round(decoded_bytes / (1024 * 1024), 3),
    }


def parse_zeek_dns_log(log_path):
    """Parse a Zeek dns.log file into structured records."""
    records = []
    with open(log_path, "r") as f:
        for line in f:
            if line.startswith("#"):
                continue
            parts = line.strip().split("\t")
            if len(parts) >= 10:
                records.append({
                    "timestamp": parts[0],
                    "src_ip": parts[2],
                    "src_port": parts[3],
                    "dst_ip": parts[4],
                    "query": parts[9] if len(parts) > 9 else "",
                    "query_type": parts[13] if len(parts) > 13 else "",
                })
    return records


if __name__ == "__main__":
    print("=" * 60)
    print("DNS Exfiltration Detection Agent")
    print("Tunneling, DGA, volume anomaly, and TXT abuse detection")
    print("=" * 60)

    # Demo with synthetic DNS records
    demo_records = [
        {"query": f"{'a' * 60}.evil-tunnel.com", "src_ip": "192.168.1.105",
         "query_type": "TXT"} for _ in range(50)
    ] + [
        {"query": "x8kj2m9p4qw7nz3.xyz", "src_ip": "192.168.1.110",
         "query_type": "A"} for _ in range(5)
    ] + [
        {"query": "google.com", "src_ip": "192.168.1.50", "query_type": "A"}
        for _ in range(10)
    ]

    print("\n--- DNS Tunneling Detection ---")
    tunneling = detect_tunneling(demo_records, subdomain_len_threshold=30, min_queries=10)
    for t in tunneling:
        print(f"[!] {t['domain']}: {t['queries']} queries, "
              f"avg subdomain len={t['avg_subdomain_length']}")

    print("\n--- DGA Detection ---")
    dga = detect_dga(demo_records, entropy_threshold=3.0, min_sld_length=10)
    for d in dga[:5]:
        print(f"[!] {d['domain']}: entropy={d['avg_entropy']}")

    print("\n--- TXT Record Abuse ---")
    txt = detect_txt_abuse(demo_records, threshold=10)
    for t in txt:
        print(f"[!] {t['src_ip']}: {t['txt_queries']} TXT queries")

    print("\n--- Entropy Examples ---")
    examples = ["google", "x8kj2m9p4qw7n", "aGVsbG8gd29ybGQ"]
    for ex in examples:
        print(f"  '{ex}' -> entropy={shannon_entropy(ex)}")