Detecting Command And Control Over Dns

Detecting Command and Control Over DNS

When to Use

• Investigating suspected DNS tunneling used for C2 communication or data exfiltration
• Analyzing DNS query logs for signs of encoded payloads in subdomain strings
• Classifying domains as DGA-generated vs. legitimate using statistical or ML methods
• Detecting DNS beaconing patterns (regular intervals, consistent query sizes)
• Hunting for Iodine, dnscat2, dns2tcp, Cobalt Strike DNS, or Sliver DNS traffic
• Monitoring TXT record abuse for command delivery or staged payload download
• Building DNS anomaly detection rules for SOC/SIEM deployment

Do not use for general DNS performance monitoring or DNS configuration auditing; use DNS health monitoring tools for those. For HTTP/HTTPS-based C2 detection, use network traffic analysis skills focused on web protocols.

DISCLAIMER: DNS tunneling tools referenced in this skill (Iodine, dnscat2, dns2tcp) are dual-use. They have legitimate uses (bypassing captive portals, security research) and malicious uses (C2 channels, exfiltration). Only deploy detection in networks you are authorized to monitor. Testing tunneling tools requires explicit authorization.

Detection Gaps & Validation

• DoH/DoT bypasses port-53 sensors entirely: C2 over https:// (443) or DoT (853) never appears in Zeek dns.log. Block/monitor known DoH resolver IPs, alert on ssl.log SNI to dns.google/cloudflare-dns.com from non-browser hosts, and treat unexplained 443/853 to public resolvers as the tunnel.
• Low-and-slow beaconing beats entropy thresholds: a beacon sending a few bytes every 30–300s with jittered intervals keeps subdomain entropy and volume below the 3.5-entropy / 50-query thresholds. Score on inter-query interval regularity (low CV) per (id.orig_h, base_domain) over hours, not single-query features.
• Dictionary-DGA and CNAME chaining evade char-level models: word-list DGAs (e.g., suppobox) have English-like bigrams; CNAME/MX answers carry data instead of the QNAME. Inspect dns.log answers/qtype_name for CNAME/TXT/NULL/MX abuse, not just query.
• Sampling/log gaps hide it: resolver logs that only record cache misses, or Zeek dropping under load, undercount query volume. Verify capture completeness via capture_loss.log before trusting "no anomaly."
• Validate the rule fires: in an authorized lab run iodine/dnscat2/dns2tcp against a controlled domain, then confirm your entropy script flags it (flags: high_entropy), the Suricata dns.query rule alerts, and cat dns.log | zeek-cut query qtype_name shows the long NULL/TXT labels. A zero result means the QNAME field path or threshold is wrong.
• FP tuning: baseline antivirus/CDN/telemetry domains (e.g., *.avts.mcafee.com, *.akamaiedge.net) that emit long high-entropy labels legitimately and whitelist before alerting.

Prerequisites

• DNS query logs from recursive resolver, Zeek/Bro, Suricata, or passive DNS tap
• Python 3.9+ with numpy, scikit-learn, pandas, tldextract, and dnspython
• Zeek (formerly Bro) with dns.log output or Suricata with DNS EVE JSON logging
• SIEM access (Splunk, Elastic, Microsoft Sentinel) for log correlation
• Passive DNS database access (CIRCL pDNS, Farsight DNSDB, or internal) for enrichment
• Wireshark/tshark for packet-level DNS inspection
• Known-good domain whitelist (Alexa/Tranco top 1M or Majestic Million)

Workflow

Step 1: Collect and Parse DNS Query Logs

Ingest DNS traffic from network sensors and parse into analyzable format:

# Zeek - extract dns.log fields
# Default Zeek dns.log columns:
# ts uid id.orig_h id.orig_p id.resp_h id.resp_p proto trans_id rtt query
# qclass qclass_name qtype qtype_name rcode rcode_name AA TC RD RA Z
# answers TTLs rejected

# Filter for potentially suspicious record types
cat dns.log | zeek-cut ts id.orig_h query qtype_name answers rcode_name | \
    grep -E "TXT|NULL|CNAME|MX" > suspicious_qtypes.log

# Extract unique queried domains
cat dns.log | zeek-cut query | sort -u > unique_domains.txt

# Suricata EVE JSON - extract DNS events
cat eve.json | jq -r 'select(.event_type=="dns") |
    [.timestamp, .src_ip, .dns.rrname, .dns.rrtype, .dns.rcode] |
    @tsv' > dns_events.tsv

# tshark - extract DNS queries from pcap
tshark -r capture.pcap -T fields \
    -e frame.time -e ip.src -e ip.dst \
    -e dns.qry.name -e dns.qry.type \
    -e dns.resp.type -e dns.txt \
    -Y "dns" > dns_queries.tsv

# Count queries per domain (find high-volume destinations)
cat dns.log | zeek-cut query | \
    awk -F. '{print $(NF-1)"."$NF}' | \
    sort | uniq -c | sort -rn | head -50

Step 2: Shannon Entropy Analysis of DNS Queries

Calculate entropy of subdomain strings to identify encoded/encrypted data:

#!/usr/bin/env python3
"""Shannon entropy analysis for DNS query subdomains."""

import math
import csv
import sys
from collections import Counter

try:
    import tldextract
    HAS_TLDEXTRACT = True
except ImportError:
    HAS_TLDEXTRACT = False


def shannon_entropy(data):
    """Calculate Shannon entropy of a string (bits per character)."""
    if not data:
        return 0.0
    counter = Counter(data)
    length = len(data)
    entropy = -sum(
        (count / length) * math.log2(count / length)
        for count in counter.values()
    )
    return entropy


def extract_subdomain(fqdn):
    """Extract the subdomain portion from a fully qualified domain name."""
    if HAS_TLDEXTRACT:
        ext = tldextract.extract(fqdn)
        if ext.subdomain:
            return ext.subdomain, f"{ext.domain}.{ext.suffix}"
        return "", f"{ext.domain}.{ext.suffix}"
    else:
        # Fallback: assume last two labels are domain + TLD
        parts = fqdn.rstrip(".").split(".")
        if len(parts) > 2:
            return ".".join(parts[:-2]), ".".join(parts[-2:])
        return "", fqdn


def analyze_dns_entropy(queries, entropy_threshold=3.5, length_threshold=30):
    """
    Analyze DNS queries for tunneling indicators using entropy.

    Thresholds (tunable per environment):
      - entropy_threshold: Shannon entropy above this flags as suspicious (3.5-4.0 typical)
      - length_threshold: Subdomain length above this flags as suspicious (30-50 chars)

    Returns list of flagged queries with scores.
    """
    results = []

    for query_record in queries:
        fqdn = query_record.get("query", "").lower().rstrip(".")
        if not fqdn:
            continue

        subdomain, base_domain = extract_subdomain(fqdn)
        if not subdomain:
            continue

        # Remove dots from subdomain for entropy calculation
        subdomain_flat = subdomain.replace(".", "")
        if not subdomain_flat:
            continue

        entropy = shannon_entropy(subdomain_flat)
        length = len(subdomain_flat)
        label_count = subdomain.count(".") + 1

        # Scoring: higher = more suspicious
        score = 0.0
        flags = []

        if entropy > entropy_threshold:
            score += (entropy - entropy_threshold) * 25
            flags.append(f"high_entropy:{entropy:.2f}")

        if length > length_threshold:
            score += (length - length_threshold) * 0.5
            flags.append(f"long_subdomain:{length}")

        if label_count > 4:
            score += label_count * 2
            flags.append(f"many_labels:{label_count}")

        # Check for hex/base32/base64 encoding patterns
        hex_ratio = sum(1 for c in subdomain_flat if c in "0123456789abcdef") / max(len(subdomain_flat), 1)
        if hex_ratio > 0.85 and length > 20:
            score += 20
            flags.append("hex_encoded")

        b32_chars = set("abcdefghijklmnopqrstuvwxyz234567")
        b32_ratio = sum(1 for c in subdomain_flat if c in b32_chars) / max(len(subdomain_flat), 1)
        if b32_ratio > 0.95 and length > 20:
            score += 15
            flags.append("base32_encoded")

        # Only report if at least one flag triggered
        if flags:
            results.append({
                "fqdn": fqdn,
                "subdomain": subdomain,
                "base_domain": base_domain,
                "entropy": round(entropy, 4),
                "subdomain_length": length,
                "label_count": label_count,
                "score": round(score, 2),
                "flags": flags,
                "src_ip": query_record.get("src_ip", ""),
                "timestamp": query_record.get("timestamp", ""),
                "qtype": query_record.get("qtype", ""),
            })

    # Sort by score descending
    results.sort(key=lambda x: x["score"], reverse=True)
    return results


# Thresholds for known tunneling tools
TOOL_SIGNATURES = {
    "iodine": {
        "subdomain_pattern": r"^[a-z0-9]{50,}$",  # Long hex-like subdomains
        "common_qtypes": ["NULL", "TXT", "CNAME", "MX", "A"],
        "typical_entropy": (3.8, 4.2),
        "description": "Iodine DNS tunnel - IPv4 over DNS, uses NULL/TXT records",
    },
    "dnscat2": {
        "subdomain_pattern": r"^dnscat\.|^[a-f0-9]{16,}",
        "common_qtypes": ["TXT", "CNAME", "MX", "A"],
        "typical_entropy": (3.5, 4.5),
        "description": "dnscat2 encrypted C2 channel over DNS",
    },
    "dns2tcp": {
        "subdomain_pattern": r"^[a-z2-7]{20,}",  # Base32 encoding
        "common_qtypes": ["TXT", "KEY"],
        "typical_entropy": (3.6, 4.0),
        "description": "dns2tcp tunnel - TCP over DNS using TXT/KEY records",
    },
    "cobalt_strike_dns": {
        "subdomain_pattern": r"^[a-f0-9]{12,}\.",
        "common_qtypes": ["A", "AAAA", "TXT"],
        "typical_entropy": (3.2, 4.0),
        "description": "Cobalt Strike DNS beacon - encoded commands in A/TXT records",
    },
}


def print_entropy_report(results, top_n=25):
    """Print formatted entropy analysis report."""
    print("=" * 80)
    print("  DNS ENTROPY ANALYSIS - TUNNELING DETECTION")
    print("=" * 80)
    print(f"  Suspicious queries found: {len(results)}")
    print()

    if not results:
        print("  No suspicious queries detected.")
        return

    # Group by base domain
    domain_groups = {}
    for r in results:
        bd = r["base_domain"]
        if bd not in domain_groups:
            domain_groups[bd] = {"count": 0, "max_entropy": 0, "max_score": 0, "queries": []}
        domain_groups[bd]["count"] += 1
        domain_groups[bd]["max_entropy"] = max(domain_groups[bd]["max_entropy"], r["entropy"])
        domain_groups[bd]["max_score"] = max(domain_groups[bd]["max_score"], r["score"])
        domain_groups[bd]["queries"].append(r)

    # Sort domains by total suspicious query count
    sorted_domains = sorted(domain_groups.items(), key=lambda x: x[1]["count"], reverse=True)

    print("  TOP SUSPICIOUS BASE DOMAINS")
    print("  " + "-" * 76)
    print(f"  {'Domain':<35} {'Queries':>8} {'Max Ent':>8} {'Max Score':>10}")
    print("  " + "-" * 76)
    for domain, data in sorted_domains[:20]:
        print(f"  {domain:<35} {data['count']:>8} {data['max_entropy']:>8.3f} {data['max_score']:>10.1f}")
    print()

    print(f"  TOP {top_n} HIGHEST-SCORING QUERIES")
    print("  " + "-" * 76)
    for r in results[:top_n]:
        print(f"  Score: {r['score']:.1f}  Entropy: {r['entropy']:.3f}  Len: {r['subdomain_length']}")
        print(f"    FQDN:   {r['fqdn'][:75]}")
        print(f"    Flags:  {', '.join(r['flags'])}")
        print(f"    Source: {r['src_ip']}  Type: {r['qtype']}")
        print()

Step 3: TXT Record Payload Detection

Identify C2 commands or staged payloads delivered via DNS TXT records:

#!/usr/bin/env python3
"""DNS TXT record payload detection for C2 command delivery."""

import base64
import re
import math
from collections import Counter


def shannon_entropy(data):
    """Calculate Shannon entropy."""
    if not data:
        return 0.0
    counter = Counter(data)
    length = len(data)
    return -sum((c / length) * math.log2(c / length) for c in counter.values())


def analyze_txt_record(txt_data, domain=""):
    """
    Analyze a DNS TXT record response for C2 payload indicators.

    Indicators:
      - High entropy content (encoded/encrypted payloads)
      - Base64-encoded executable content
      - PowerShell stager patterns
      - Unusually large TXT records (>255 bytes per string, multiple strings)
      - Known C2 framework patterns
    """
    findings = {
        "domain": domain,
        "txt_length": len(txt_data),
        "entropy": shannon_entropy(txt_data),
        "suspicious": False,
        "indicators": [],
        "decoded_preview": None,
    }

    # Length check - legitimate TXT records are typically short (SPF, DKIM, verification)
    if len(txt_data) > 500:
        findings["indicators"].append({
            "type": "oversized_txt",
            "detail": f"TXT record length {len(txt_data)} exceeds normal threshold (500)",
            "severity": "medium",
        })

    # High entropy - suggests encoded/encrypted payload
    if findings["entropy"] > 4.5 and len(txt_data) > 100:
        findings["indicators"].append({
            "type": "high_entropy_payload",
            "detail": f"Entropy {findings['entropy']:.3f} suggests encoded data",
            "severity": "high",
        })

    # Base64 detection
    b64_pattern = re.compile(r'^[A-Za-z0-9+/]{40,}={0,2}$')
    if b64_pattern.match(txt_data.strip()):
        findings["indicators"].append({
            "type": "base64_encoded",
            "detail": "Content matches base64 pattern",
            "severity": "high",
        })
        try:
            decoded = base64.b64decode(txt_data.strip())
            preview = decoded[:200]

            # Check for PE header (MZ)
            if preview[:2] == b'MZ':
                findings["indicators"].append({
                    "type": "pe_executable",
                    "detail": "Decoded base64 contains PE executable (MZ header)",
                    "severity": "critical",
                })

            # Check for ELF header
            if preview[:4] == b'\x7fELF':
                findings["indicators"].append({
                    "type": "elf_executable",
                    "detail": "Decoded base64 contains ELF executable",
                    "severity": "critical",
                })

            # Check for PowerShell patterns
            decoded_str = decoded.decode("utf-8", errors="ignore")
            ps_patterns = [
                r"Invoke-Expression",
                r"IEX\s*\(",
                r"New-Object\s+System\.Net",
                r"DownloadString",
                r"FromBase64String",
                r"Start-Process",
                r"\-enc\s",
                r"powershell\s.*\-e\s",
            ]
            for pattern in ps_patterns:
                if re.search(pattern, decoded_str, re.IGNORECASE):
                    findings["indicators"].append({
                        "type": "powershell_stager",
                        "detail": f"Decoded content contains PowerShell pattern: {pattern}",
                        "severity": "critical",
                    })
                    break

            findings["decoded_preview"] = repr(preview[:100])

        except Exception:
            pass

    # Known C2 TXT patterns
    cobalt_pattern = re.compile(r'^[a-f0-9]{32,}$', re.IGNORECASE)
    if cobalt_pattern.match(txt_data.strip()):
        findings["indicators"].append({
            "type": "hex_encoded_payload",
            "detail": "Pure hex string in TXT record - possible Cobalt Strike beacon config",
            "severity": "high",
        })

    # Multiple concatenated base64 blocks (common in staged delivery)
    b64_blocks = re.findall(r'[A-Za-z0-9+/]{50,}={0,2}', txt_data)
    if len(b64_blocks) > 3:
        findings["indicators"].append({
            "type": "multi_block_payload",
            "detail": f"{len(b64_blocks)} base64 blocks found - possible staged payload",
            "severity": "high",
        })

    # Check for known legitimate TXT patterns to reduce false positives
    legitimate_patterns = [
        r'^v=spf1\s',           # SPF record
        r'^v=DKIM1',            # DKIM record
        r'^v=DMARC1',           # DMARC record
        r'^google-site-verification=',
        r'^MS=',                # Microsoft domain verification
        r'^docusign=',
        r'^apple-domain-verification=',
        r'^facebook-domain-verification=',
        r'^_globalsign-domain-verification=',
    ]
    for pattern in legitimate_patterns:
        if re.match(pattern, txt_data, re.IGNORECASE):
            findings["indicators"] = []
            findings["legitimate"] = True
            return findings

    findings["suspicious"] = len(findings["indicators"]) > 0
    return findings


def analyze_txt_records_bulk(records):
    """Analyze a batch of DNS TXT records."""
    results = []
    for record in records:
        domain = record.get("domain", record.get("query", ""))
        txt_data = record.get("txt", record.get("answer", ""))
        if txt_data:
            finding = analyze_txt_record(txt_data, domain)
            if finding["suspicious"]:
                results.append(finding)

    results.sort(
        key=lambda x: max((i.get("severity_score", 0) for i in x["indicators"]),
                          default=0),
        reverse=True,
    )
    return results

Step 4: DGA Domain Classification with Machine Learning

Train a classifier to distinguish DGA-generated domains from legitimate ones:

#!/usr/bin/env python3
"""
DGA domain classification using character-level feature extraction and ML.

Features extracted per domain:
  - Shannon entropy of the domain string
  - Domain length
  - Digit ratio, consonant ratio, vowel ratio
  - Longest consecutive consonant sequence
  - N-gram frequency deviation from English
  - Number of distinct characters
  - Presence of dictionary words
"""

import math
import re
import string
from collections import Counter

import numpy as np

try:
    from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
    from sklearn.model_selection import train_test_split, cross_val_score
    from sklearn.metrics import classification_report, confusion_matrix
    from sklearn.preprocessing import StandardScaler
    HAS_SKLEARN = True
except ImportError:
    HAS_SKLEARN = False


# English language character bigram frequencies (normalized, top bigrams)
# Source: Peter Norvig's English letter frequency analysis
ENGLISH_BIGRAMS = {
    "th": 0.0356, "he": 0.0307, "in": 0.0243, "er": 0.0205,
    "an": 0.0199, "re": 0.0185, "on": 0.0176, "at": 0.0149,
    "en": 0.0145, "nd": 0.0135, "ti": 0.0134, "es": 0.0134,
    "or": 0.0128, "te": 0.0120, "of": 0.0117, "ed": 0.0117,
    "is": 0.0113, "it": 0.0112, "al": 0.0109, "ar": 0.0107,
    "st": 0.0105, "to": 0.0104, "nt": 0.0104, "ng": 0.0095,
    "se": 0.0093, "ha": 0.0093, "as": 0.0087, "ou": 0.0087,
    "io": 0.0083, "le": 0.0083, "ve": 0.0083, "co": 0.0079,
    "me": 0.0079, "de": 0.0076, "hi": 0.0076, "ri": 0.0073,
    "ro": 0.0073, "ic": 0.0070, "ne": 0.0069, "ea": 0.0069,
}

VOWELS = set("aeiou")
CONSONANTS = set("bcdfghjklmnpqrstvwxyz")


def extract_domain_features(domain):
    """Extract numerical features from a domain name for ML classification."""
    domain = domain.lower().strip(".")

    # Remove TLD for analysis (focus on SLD + subdomain)
    parts = domain.split(".")
    if len(parts) > 1:
        analysis_str = ".".join(parts[:-1])  # Drop TLD
    else:
        analysis_str = domain

    # Remove dots for character analysis
    flat = analysis_str.replace(".", "")
    length = len(flat)

    if length == 0:
        return None

    # 1. Shannon entropy
    entropy = 0.0
    counter = Counter(flat)
    for count in counter.values():
        p = count / length
        entropy -= p * math.log2(p)

    # 2. Character ratios
    digit_count = sum(1 for c in flat if c.isdigit())
    vowel_count = sum(1 for c in flat if c in VOWELS)
    consonant_count = sum(1 for c in flat if c in CONSONANTS)
    special_count = sum(1 for c in flat if c == '-')

    digit_ratio = digit_count / length
    vowel_ratio = vowel_count / length
    consonant_ratio = consonant_count / length

    # 3. Longest consecutive consonant run
    max_consonant_run = 0
    current_run = 0
    for c in flat:
        if c in CONSONANTS:
            current_run += 1
            max_consonant_run = max(max_consonant_run, current_run)
        else:
            current_run = 0

    # 4. Distinct character count and ratio
    distinct_chars = len(set(flat))
    distinct_ratio = distinct_chars / length

    # 5. Bigram frequency deviation from English
    bigrams = [flat[i:i+2] for i in range(len(flat) - 1)]
    if bigrams:
        english_score = sum(
            ENGLISH_BIGRAMS.get(bg, 0) for bg in bigrams
        ) / len(bigrams)
    else:
        english_score = 0

    # 6. Number of labels (dots + 1)
    label_count = len(parts)

    # 7. Hex character ratio (common in DGA)
    hex_chars = set("0123456789abcdef")
    hex_ratio = sum(1 for c in flat if c in hex_chars) / length

    # 8. Digit-letter transitions (DGA domains mix digits and letters)
    transitions = 0
    for i in range(1, len(flat)):
        if (flat[i].isdigit() != flat[i-1].isdigit()):
            transitions += 1
    transition_ratio = transitions / max(length - 1, 1)

    # 9. Repeated character ratio
    if length > 1:
        repeats = sum(1 for i in range(1, len(flat)) if flat[i] == flat[i-1])
        repeat_ratio = repeats / (length - 1)
    else:
        repeat_ratio = 0

    return {
        "domain": domain,
        "length": length,
        "entropy": round(entropy, 4),
        "digit_ratio": round(digit_ratio, 4),
        "vowel_ratio": round(vowel_ratio, 4),
        "consonant_ratio": round(consonant_ratio, 4),
        "max_consonant_run": max_consonant_run,
        "distinct_chars": distinct_chars,
        "distinct_ratio": round(distinct_ratio, 4),
        "english_bigram_score": round(english_score, 6),
        "label_count": label_count,
        "hex_ratio": round(hex_ratio, 4),
        "transition_ratio": round(transition_ratio, 4),
        "repeat_ratio": round(repeat_ratio, 4),
        "special_count": special_count,
    }


FEATURE_COLUMNS = [
    "length", "entropy", "digit_ratio", "vowel_ratio", "consonant_ratio",
    "max_consonant_run", "distinct_chars", "distinct_ratio",
    "english_bigram_score", "label_count", "hex_ratio",
    "transition_ratio", "repeat_ratio", "special_count",
]


def features_to_vector(features):
    """Convert feature dict to numpy array."""
    return np.array([features[col] for col in FEATURE_COLUMNS])


def train_dga_classifier(legitimate_domains, dga_domains, model_type="random_forest"):
    """
    Train a DGA classifier on labeled domain lists.

    Args:
        legitimate_domains: list of known-good domain strings
        dga_domains: list of known DGA domain strings
        model_type: 'random_forest' or 'gradient_boosting'

    Returns:
        trained model, scaler, and evaluation metrics
    """
    if not HAS_SKLEARN:
        print("[ERROR] scikit-learn required: pip install scikit-learn")
        return None, None, None

    # Extract features
    X_legit = []
    X_dga = []

    for d in legitimate_domains:
        feats = extract_domain_features(d)
        if feats:
            X_legit.append(features_to_vector(feats))

    for d in dga_domains:
        feats = extract_domain_features(d)
        if feats:
            X_dga.append(features_to_vector(feats))

    if not X_legit or not X_dga:
        print("[ERROR] Insufficient feature data")
        return None, None, None

    X = np.vstack([np.array(X_legit), np.array(X_dga)])
    y = np.array([0] * len(X_legit) + [1] * len(X_dga))

    # Scale features
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)

    # Train/test split
    X_train, X_test, y_train, y_test = train_test_split(
        X_scaled, y, test_size=0.2, random_state=42, stratify=y
    )

    # Train model
    if model_type == "gradient_boosting":
        model = GradientBoostingClassifier(
            n_estimators=200, max_depth=6, learning_rate=0.1,
            min_samples_split=10, random_state=42,
        )
    else:
        model = RandomForestClassifier(
            n_estimators=200, max_depth=15, min_samples_split=5,
            random_state=42, n_jobs=-1,
        )

    model.fit(X_train, y_train)

    # Evaluate
    y_pred = model.predict(X_test)
    report = classification_report(y_test, y_pred, target_names=["legitimate", "dga"],
                                   output_dict=True)
    cm = confusion_matrix(y_test, y_pred)

    # Cross-validation
    cv_scores = cross_val_score(model, X_scaled, y, cv=5, scoring="f1")

    metrics = {
        "accuracy": report["accuracy"],
        "precision_dga": report["dga"]["precision"],
        "recall_dga": report["dga"]["recall"],
        "f1_dga": report["dga"]["f1-score"],
        "precision_legit": report["legitimate"]["precision"],
        "recall_legit": report["legitimate"]["recall"],
        "confusion_matrix": cm.tolist(),
        "cv_f1_mean": cv_scores.mean(),
        "cv_f1_std": cv_scores.std(),
        "feature_importance": dict(zip(
            FEATURE_COLUMNS,
            [round(float(x), 4) for x in model.feature_importances_]
        )),
    }

    print(f"[+] Model trained: {model_type}")
    print(f"    Accuracy:     {metrics['accuracy']:.4f}")
    print(f"    DGA F1:       {metrics['f1_dga']:.4f}")
    print(f"    DGA Recall:   {metrics['recall_dga']:.4f}")
    print(f"    CV F1 (5-fold): {metrics['cv_f1_mean']:.4f} +/- {metrics['cv_f1_std']:.4f}")
    print(f"    Top features: ", end="")
    top_feats = sorted(metrics["feature_importance"].items(), key=lambda x: x[1], reverse=True)[:5]
    print(", ".join(f"{k}={v:.3f}" for k, v in top_feats))

    return model, scaler, metrics


def classify_domains(domains, model, scaler):
    """Classify a list of domains as legitimate or DGA using a trained model."""
    results = []
    for domain in domains:
        feats = extract_domain_features(domain)
        if feats is None:
            continue

        vec = features_to_vector(feats).reshape(1, -1)
        vec_scaled = scaler.transform(vec)

        prediction = model.predict(vec_scaled)[0]
        probability = model.predict_proba(vec_scaled)[0]

        results.append({
            "domain": domain,
            "prediction": "dga" if prediction == 1 else "legitimate",
            "confidence": round(float(max(probability)), 4),
            "dga_probability": round(float(probability[1]), 4),
            "features": feats,
        })

    return results

Step 5: DNS Beaconing Pattern Detection

Identify periodic DNS query patterns indicative of C2 check-ins:

#!/usr/bin/env python3
"""DNS beaconing detection - identifies periodic C2 check-in patterns."""

import math
from collections import defaultdict
from datetime import datetime, timedelta

import numpy as np


def parse_timestamp(ts_str):
    """Parse various timestamp formats to datetime."""
    formats = [
        "%Y-%m-%dT%H:%M:%S.%fZ",
        "%Y-%m-%dT%H:%M:%S.%f",
        "%Y-%m-%dT%H:%M:%S",
        "%Y-%m-%d %H:%M:%S.%f",
        "%Y-%m-%d %H:%M:%S",
    ]
    for fmt in formats:
        try:
            return datetime.strptime(ts_str, fmt)
        except ValueError:
            continue

    # Try epoch timestamp
    try:
        ts_float = float(ts_str)
        return datetime.utcfromtimestamp(ts_float)
    except (ValueError, OverflowError, OSError):
        pass

    return None


def detect_beaconing(dns_queries, min_queries=10, max_jitter_pct=25,
                     min_interval_sec=10, max_interval_sec=7200):
    """
    Detect DNS beaconing by analyzing inter-query timing intervals.

    Beaconing indicators:
      - Regular inter-query intervals (low standard deviation)
      - Consistent query sizes
      - Single source IP to single domain over extended period
      - Low jitter (variation in timing)

    Args:
        dns_queries: list of dicts with 'src_ip', 'query', 'timestamp'
        min_queries: minimum queries to analyze (default 10)
        max_jitter_pct: maximum coefficient of variation for beacon (default 25%)
        min_interval_sec: minimum beacon interval to detect (default 10s)
        max_interval_sec: maximum beacon interval to detect (default 7200s / 2hr)

    Returns:
        list of detected beacon patterns with confidence scores
    """
    # Group queries by (source IP, base domain)
    groups = defaultdict(list)

    for q in dns_queries:
        src_ip = q.get("src_ip", "")
        fqdn = q.get("query", "").lower().rstrip(".")
        ts_str = q.get("timestamp", "")

        ts = parse_timestamp(ts_str)
        if not ts or not src_ip or not fqdn:
            continue

        # Extract base domain (last 2 labels)
        parts = fqdn.split(".")
        if len(parts) >= 2:
            base_domain = ".".join(parts[-2:])
        else:
            base_domain = fqdn

        groups[(src_ip, base_domain)].append(ts)

    beacons = []

    for (src_ip, base_domain), timestamps in groups.items():
        if len(timestamps) < min_queries:
            continue

        # Sort timestamps and compute intervals
        timestamps.sort()
        intervals = [
            (timestamps[i+1] - timestamps[i]).total_seconds()
            for i in range(len(timestamps) - 1)
        ]

        if not intervals:
            continue

        intervals = np.array(intervals)

        # Filter out zero intervals (duplicate timestamps)
        intervals = intervals[intervals > 0]
        if len(intervals) < min_queries - 1:
            continue

        mean_interval = np.mean(intervals)
        std_interval = np.std(intervals)
        median_interval = np.median(intervals)

        # Skip if interval is outside detection range
        if mean_interval < min_interval_sec or mean_interval > max_interval_sec:
            continue

        # Coefficient of variation (jitter)
        cv = (std_interval / mean_interval * 100) if mean_interval > 0 else 100

        # Time span of activity
        time_span = (timestamps[-1] - timestamps[0]).total_seconds()
        hours_active = time_span / 3600

        # Beacon scoring
        score = 0.0
        flags = []

        # Low jitter = strong beacon indicator
        if cv < 5:
            score += 40
            flags.append(f"very_low_jitter:CV={cv:.1f}%")
        elif cv < 15:
            score += 30
            flags.append(f"low_jitter:CV={cv:.1f}%")
        elif cv < max_jitter_pct:
            score += 15
            flags.append(f"moderate_jitter:CV={cv:.1f}%")
        else:
            continue  # Too much jitter, not a beacon

        # Long duration increases confidence
        if hours_active > 24:
            score += 20
            flags.append(f"persistent:{hours_active:.1f}h")
        elif hours_active > 4:
            score += 10
            flags.append(f"sustained:{hours_active:.1f}h")

        # High query count increases confidence
        if len(timestamps) > 100:
            score += 15
            flags.append(f"high_volume:{len(timestamps)}")
        elif len(timestamps) > 50:
            score += 10
            flags.append(f"moderate_volume:{len(timestamps)}")

        # Common C2 intervals (60s, 120s, 300s, 600s, 900s, 1800s, 3600s)
        common_intervals = [60, 120, 300, 600, 900, 1800, 3600]
        for ci in common_intervals:
            if abs(mean_interval - ci) < ci * 0.1:  # Within 10% of common interval
                score += 10
                flags.append(f"common_c2_interval:~{ci}s")
                break

        beacons.append({
            "src_ip": src_ip,
            "base_domain": base_domain,
            "query_count": len(timestamps),
            "mean_interval_sec": round(mean_interval, 2),
            "median_interval_sec": round(median_interval, 2),
            "std_interval_sec": round(std_interval, 2),
            "jitter_cv_pct": round(cv, 2),
            "first_seen": timestamps[0].isoformat(),
            "last_seen": timestamps[-1].isoformat(),
            "duration_hours": round(hours_active, 2),
            "score": round(score, 1),
            "flags": flags,
        })

    beacons.sort(key=lambda x: x["score"], reverse=True)
    return beacons


def print_beacon_report(beacons, top_n=20):
    """Print formatted beacon detection report."""
    print("=" * 80)
    print("  DNS BEACONING DETECTION REPORT")
    print("=" * 80)
    print(f"  Beacon patterns detected: {len(beacons)}")
    print()

    if not beacons:
        print("  No beaconing patterns detected.")
        return

    print(f"  TOP {min(top_n, len(beacons))} BEACON CANDIDATES")
    print("  " + "-" * 76)

    for b in beacons[:top_n]:
        print(f"  Score: {b['score']:.1f}  |  {b['src_ip']} -> {b['base_domain']}")
        print(f"    Queries: {b['query_count']}  "
              f"Interval: {b['mean_interval_sec']:.1f}s +/- {b['std_interval_sec']:.1f}s  "
              f"Jitter: {b['jitter_cv_pct']:.1f}%")
        print(f"    Active: {b['duration_hours']:.1f}h  "
              f"({b['first_seen']} to {b['last_seen']})")
        print(f"    Flags: {', '.join(b['flags'])}")
        print()

Step 6: Integrated DNS C2 Detection Pipeline

Combine all detection methods into a unified analysis:

DNS C2 Detection Pipeline Architecture:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

  ┌────────────────────────────────────────────────────────┐
  │                  DATA SOURCES                          │
  │  Zeek dns.log  |  Suricata EVE  |  Recursive Resolver │
  │  Passive DNS   |  PCAP capture  |  EDR DNS telemetry  │
  └───────────────────────┬────────────────────────────────┘
                          │
  ┌───────────────────────▼────────────────────────────────┐
  │              PREPROCESSING                             │
  │  Parse timestamps | Extract subdomains | Normalize     │
  │  FQDN | Resolve base domain | Lookup in whitelist     │
  └───────────────────────┬────────────────────────────────┘
                          │
          ┌───────────────┼───────────────┐
          │               │               │
  ┌───────▼──────┐ ┌──────▼──────┐ ┌──────▼──────┐
  │  ENTROPY     │ │  BEACONING  │ │  DGA        │
  │  ANALYSIS    │ │  DETECTION  │ │  CLASSIFIER │
  │              │ │             │ │             │
  │ Shannon ent. │ │ Interval    │ │ ML model    │
  │ Subdomain    │ │ analysis    │ │ Random      │
  │ length       │ │ Jitter/CV   │ │ Forest or   │
  │ Encoding     │ │ Duration    │ │ Gradient    │
  │ patterns     │ │ Periodicity │ │ Boosting    │
  └───────┬──────┘ └──────┬──────┘ └──────┬──────┘
          │               │               │
  ┌───────▼──────┐ ┌──────▼──────┐ ┌──────▼──────┐
  │ TXT RECORD   │ │  TOOL       │ │  PASSIVE    │
  │ PAYLOAD      │ │  SIGNATURE  │ │  DNS        │
  │ ANALYSIS     │ │  MATCHING   │ │  ENRICHMENT │
  │              │ │             │ │             │
  │ Base64 decode│ │ Iodine      │ │ First seen  │
  │ PE/ELF detect│ │ dnscat2     │ │ Registrar   │
  │ PS stager    │ │ dns2tcp     │ │ Age check   │
  │ Size anomaly │ │ Cobalt DNS  │ │ Reputation  │
  └───────┬──────┘ └──────┬──────┘ └──────┬──────┘
          │               │               │
  ┌───────▼───────────────▼───────────────▼────────────────┐
  │                CORRELATION ENGINE                       │
  │  Combine scores from all detectors                     │
  │  Weighted scoring: entropy(30%) + beacon(25%) +        │
  │    DGA(20%) + TXT payload(15%) + signature(10%)        │
  │  Threshold: score > 60 = alert, > 40 = investigate     │
  └───────────────────────┬────────────────────────────────┘
                          │
  ┌───────────────────────▼────────────────────────────────┐
  │                ALERTING & RESPONSE                     │
  │  Generate SIEM alert with all evidence                 │
  │  Block domain in DNS firewall / RPZ                    │
  │  Isolate endpoint via EDR                              │
  │  Create incident ticket with IOCs                      │
  └────────────────────────────────────────────────────────┘

Step 7: SIEM Detection Rules

Deploy detection queries in your SIEM platform:

Splunk SPL - DNS Tunneling Detection:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

-- High entropy subdomain queries
index=dns sourcetype="bro:dns:json" OR sourcetype="suricata:dns"
| eval subdomain=mvindex(split(query,"."),0)
| eval sub_len=len(subdomain)
| where sub_len > 30
| eval char_counts=mvmap(split(subdomain,""),1)
| lookup dns_entropy_lookup subdomain OUTPUT entropy
| where entropy > 3.5
| stats count as query_count dc(query) as unique_queries
    avg(sub_len) as avg_sub_len values(query) as sample_queries
    by src_ip, domain
| where query_count > 20
| sort -query_count

-- DNS TXT record abuse
index=dns (qtype="TXT" OR qtype_name="TXT")
  NOT (query="*._domainkey.*" OR query="*._dmarc.*" OR query="*._spf.*")
| stats count as txt_queries dc(query) as unique_txt_queries
    values(query) as domains
    by src_ip
| where txt_queries > 50
| sort -txt_queries

-- DNS beaconing (regular interval queries)
index=dns sourcetype="bro:dns:json"
| bin _time span=60s
| stats count by src_ip, query, _time
| streamstats window=10 current=t avg(count) as avg_count stdev(count) as std_count by src_ip, query
| eval cv = if(avg_count>0, (std_count/avg_count)*100, 100)
| where cv < 20 AND avg_count > 0
| stats count as beacon_windows avg(cv) as avg_jitter
    min(_time) as first_seen max(_time) as last_seen
    by src_ip, query
| where beacon_windows > 10
| sort -beacon_windows

-- Unusual record type volume (NULL, KEY, SRV for tunneling)
index=dns (qtype_name="NULL" OR qtype_name="KEY" OR qtype_name="SRV"
    OR qtype_name="CNAME" OR qtype_name="MX")
  NOT qtype_name="A" NOT qtype_name="AAAA" NOT qtype_name="PTR"
| stats count by src_ip, qtype_name, query
| where count > 10
| sort -count

Elastic KQL - DNS C2 Detection:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

-- Long subdomain queries (potential tunneling)
dns.question.name: * and not dns.question.name: *.in-addr.arpa
| where length(dns.question.subdomain) > 40

-- High volume DNS to single domain
event.dataset: "zeek.dns" or event.dataset: "suricata.dns"
| stats count by source.ip, dns.question.registered_domain
| where count > 500

-- TXT record queries to non-standard domains
dns.question.type: "TXT"
  and not dns.question.name: (*._domainkey.* or *._dmarc.* or *._spf.*)

Zeek Script - DNS Tunneling Indicator:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

# dns_tunnel_detect.zeek
@load base/protocols/dns

module DNSTunnel;

export {
    redef enum Notice::Type += {
        DNS_Tunneling_Suspected,
        DNS_High_Entropy_Query,
        DNS_Excessive_TXT_Queries,
    };

    const entropy_threshold = 3.5 &redef;
    const subdomain_length_threshold = 40 &redef;
    const txt_query_threshold = 50 &redef;
    const tracking_interval = 5min &redef;
}

global txt_query_tracker: table[addr] of count &create_expire=5min &default=0;
global domain_query_tracker: table[addr, string] of count &create_expire=10min &default=0;

function shannon_entropy(s: string): double
{
    local counts: table[string] of count;
    local total = |s|;

    if (total == 0) return 0.0;

    for (i in s)
        {
        local c = s[i];
        if (c !in counts) counts[c] = 0;
        ++counts[c];
        }

    local ent = 0.0;
    for (ch, cnt in counts)
        {
        local p = cnt * 1.0 / total;
        ent -= p * log2(p);
        }

    return ent;
}

event dns_request(c: connection, msg: dns_msg, query: string, qtype: count,
                  qclass: count)
{
    if (|query| == 0) return;

    # Track TXT queries
    if (qtype == 16)  # TXT
    {
        ++txt_query_tracker[c$id

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