detecting-modbus-protocol-anomalies
Detect anomalies in Modbus/TCP and Modbus RTU industrial traffic via function code monitoring, register range validation, timing analysis, and deep packet inspection, using Zeek's Modbus analyzer, Suricata IDS with OT rules, and Python Markov chain models of normal transaction sequences. Use for deploying Modbus IDS in OT environments, baselining polling patterns, investigating suspicious Modbus traffic, or building function code allowlists.
- License 许可
- Apache-2.0
- 最近更新
- 2026-08-31
- Stars
- ★ 3.6w
- Forks
- 4.4k
01 前言 Front Matter
- name
- detecting-modbus-protocol-anomalies
- tags
- [ "ot-security", "ics", "scada", "industrial-control", "iec62443", "modbus", "protocol-anomaly" ]
- author
- mahipal
- domain
- cybersecurity
- license
- Apache-2.0
- version
- 1.0.0
- nist_csf
- [ "PR.IR-01", "DE.CM-01", "ID.AM-05", "GV.OC-02" ]
- subdomain
- ot-ics-security
- description
- Detect anomalies in Modbus/TCP and Modbus RTU industrial traffic via function code monitoring, register range validation, timing analysis, and deep packet inspection, using Zeek's Modbus analyzer, Suricata IDS with OT rules, and Python Markov chain models of normal transaction sequences. Use for deploying Modbus IDS in OT environments, baselining polling patterns, investigating suspicious Modbus traffic, or building function code allowlists.
- nist_ai_rmf
- [ "MEASURE-2.7", "MAP-5.1", "MANAGE-2.4" ]
- mitre_attack
- [ "T1078", "T1190", "T1059", "T0816", "T0836" ]
- atlas_techniques
- [ "AML.T0070", "AML.T0066", "AML.T0082" ]
02 资源文件 3
- 许可证LICENSE11.0 KB
- 参考文档references/api-reference.md1.6 KB
- 脚本scripts/agent.py5.4 KB
03 SKILL.md 正文
Detecting Modbus Protocol Anomalies
When to Use
- When deploying Modbus-specific intrusion detection in an OT environment
- When building baseline models for deterministic Modbus polling patterns
- When investigating suspicious Modbus traffic flagged by OT monitoring tools
- When implementing function code allowlisting on industrial firewalls
- When detecting unauthorized Modbus write commands that could manipulate process setpoints
Do not use for securing Modbus communications end-to-end (Modbus has no native security; see implementing-network-segmentation-for-ot for firewall-based controls), for non-Modbus protocol monitoring (see detecting-anomalies-in-industrial-control-systems for multi-protocol), or for active fuzzing of Modbus implementations (see performing-plc-firmware-security-analysis).
Prerequisites
- Network SPAN/TAP access to monitor Modbus/TCP traffic (port 502)
- Zeek (formerly Bro) with Modbus protocol analyzer or Suricata with OT rulesets
- Python 3.9+ with scapy and pymodbus for custom analysis
- Baseline capture of normal Modbus traffic (minimum 1-2 weeks)
- Documentation of authorized Modbus clients, function codes, and register maps
Workflow
Step 1: Capture and Parse Modbus Traffic
Deploy passive monitoring to capture all Modbus/TCP traffic and parse it into structured records for analysis.
#!/usr/bin/env python3
"""Modbus Protocol Anomaly Detection System.
Monitors Modbus/TCP traffic for anomalies including unauthorized
function codes, unusual register access, timing deviations,
and rogue client devices.
"""
import json
import struct
import sys
import time
from collections import defaultdict, deque
from dataclasses import dataclass, field
from datetime import datetime
from statistics import mean, stdev
try:
from scapy.all import sniff, rdpcap, IP, TCP
except ImportError:
print("Install scapy: pip install scapy")
sys.exit(1)
MODBUS_FUNCTION_CODES = {
1: ("Read Coils", "read"),
2: ("Read Discrete Inputs", "read"),
3: ("Read Holding Registers", "read"),
4: ("Read Input Registers", "read"),
5: ("Write Single Coil", "write"),
6: ("Write Single Register", "write"),
7: ("Read Exception Status", "diagnostic"),
8: ("Diagnostics", "diagnostic"),
11: ("Get Comm Event Counter", "diagnostic"),
12: ("Get Comm Event Log", "diagnostic"),
15: ("Write Multiple Coils", "write"),
16: ("Write Multiple Registers", "write"),
17: ("Report Slave ID", "diagnostic"),
22: ("Mask Write Register", "write"),
23: ("Read/Write Multiple Registers", "read_write"),
43: ("Encapsulated Interface Transport", "diagnostic"),
}
@dataclass
class ModbusAnomaly:
timestamp: str
anomaly_type: str
severity: str
src_ip: str
dst_ip: str
unit_id: int
func_code: int
detail: str
mitre_technique: str = ""
@dataclass
class ModbusSession:
"""Tracks state for a Modbus master-slave session."""
src_ip: str
dst_ip: str
func_codes_seen: dict = field(default_factory=lambda: defaultdict(int))
register_ranges: set = field(default_factory=set)
intervals: list = field(default_factory=lambda: deque(maxlen=500))
last_timestamp: float = 0
request_count: int = 0
write_count: int = 0
class ModbusAnomalyDetector:
"""Detects anomalies in Modbus/TCP traffic."""
def __init__(self):
self.sessions = {}
self.baseline_sessions = {}
self.anomalies = []
self.authorized_clients = set()
self.authorized_func_codes = {} # per-session allowed FCs
self.packet_count = 0
def set_authorized_clients(self, clients):
"""Set list of authorized Modbus client IPs."""
self.authorized_clients = set(clients)
def set_authorized_func_codes(self, session_key, func_codes):
"""Set allowed function codes for a specific session."""
self.authorized_func_codes[session_key] = set(func_codes)
def load_baseline(self, baseline_file):
"""Load baseline profiles from previous capture analysis."""
with open(baseline_file) as f:
baseline = json.load(f)
for key, data in baseline.get("modbus_baselines", {}).items():
self.baseline_sessions[key] = data
self.authorized_func_codes[key] = set(data.get("allowed_function_codes", []))
print(f"[*] Loaded {len(self.baseline_sessions)} Modbus baselines")
def process_packet(self, pkt):
"""Process a single packet for Modbus anomaly detection."""
if not pkt.haslayer(TCP) or not pkt.haslayer(IP):
return
# Check for Modbus/TCP (port 502)
if pkt[TCP].dport != 502 and pkt[TCP].sport != 502:
return
payload = bytes(pkt[TCP].payload)
if len(payload) < 8:
return
self.packet_count += 1
timestamp = float(pkt.time)
ts_str = datetime.fromtimestamp(timestamp).isoformat()
# Parse MBAP header
try:
trans_id = struct.unpack(">H", payload[0:2])[0]
proto_id = struct.unpack(">H", payload[2:4])[0]
length = struct.unpack(">H", payload[4:6])[0]
unit_id = payload[6]
func_code = payload[7]
except (IndexError, struct.error):
return
# Determine direction
if pkt[TCP].dport == 502:
src_ip = pkt[IP].src
dst_ip = pkt[IP].dst
is_request = True
else:
src_ip = pkt[IP].dst
dst_ip = pkt[IP].src
is_request = False
if not is_request:
return # Only analyze requests
session_key = f"{src_ip}->{dst_ip}"
# Get or create session
if session_key not in self.sessions:
self.sessions[session_key] = ModbusSession(src_ip=src_ip, dst_ip=dst_ip)
session = self.sessions[session_key]
session.request_count += 1
session.func_codes_seen[func_code] += 1
# ── Anomaly Detection Rules ──
# Rule 1: Unauthorized Modbus client
if self.authorized_clients and src_ip not in self.authorized_clients:
self.anomalies.append(ModbusAnomaly(
timestamp=ts_str,
anomaly_type="UNAUTHORIZED_CLIENT",
severity="critical",
src_ip=src_ip, dst_ip=dst_ip,
unit_id=unit_id, func_code=func_code,
detail=f"Modbus request from unauthorized client {src_ip}",
mitre_technique="T0886 - Remote Services",
))
# Rule 2: Unauthorized function code
allowed_fcs = self.authorized_func_codes.get(session_key)
if allowed_fcs and func_code not in allowed_fcs:
fc_info = MODBUS_FUNCTION_CODES.get(func_code, (f"Unknown FC{func_code}", "unknown"))
severity = "critical" if fc_info[1] == "write" else "high"
self.anomalies.append(ModbusAnomaly(
timestamp=ts_str,
anomaly_type="UNAUTHORIZED_FUNCTION_CODE",
severity=severity,
src_ip=src_ip, dst_ip=dst_ip,
unit_id=unit_id, func_code=func_code,
detail=f"FC {func_code} ({fc_info[0]}) not in allowlist {sorted(allowed_fcs)}",
mitre_technique="T0855 - Unauthorized Command Message",
))
# Rule 3: Write operation detection
if func_code in (5, 6, 15, 16, 22, 23):
session.write_count += 1
fc_name = MODBUS_FUNCTION_CODES.get(func_code, ("Unknown", ""))[0]
# Extract register address
if len(payload) >= 10:
register_addr = struct.unpack(">H", payload[8:10])[0]
session.register_ranges.add((func_code, register_addr))
self.anomalies.append(ModbusAnomaly(
timestamp=ts_str,
anomaly_type="WRITE_OPERATION",
severity="high",
src_ip=src_ip, dst_ip=dst_ip,
unit_id=unit_id, func_code=func_code,
detail=f"Write: {fc_name} to register {register_addr} from {src_ip}",
mitre_technique="T0836 - Modify Parameter",
))
# Rule 4: Timing anomaly
if session.last_timestamp > 0:
interval = (timestamp - session.last_timestamp) * 1000 # ms
session.intervals.append(interval)
baseline = self.baseline_sessions.get(session_key)
if baseline and len(session.intervals) > 10:
expected_interval = baseline.get("polling_interval_avg_sec", 0) * 1000
expected_std = baseline.get("polling_interval_stddev", 0) * 1000
if expected_std > 0:
z_score = abs(interval - expected_interval) / expected_std
if z_score > 5.0:
self.anomalies.append(ModbusAnomaly(
timestamp=ts_str,
anomaly_type="TIMING_ANOMALY",
severity="medium",
src_ip=src_ip, dst_ip=dst_ip,
unit_id=unit_id, func_code=func_code,
detail=(
f"Interval {interval:.0f}ms vs baseline "
f"{expected_interval:.0f}ms (z={z_score:.1f})"
),
mitre_technique="T0831 - Manipulation of Control",
))
# Rule 5: Protocol violation - invalid protocol ID
if proto_id != 0:
self.anomalies.append(ModbusAnomaly(
timestamp=ts_str,
anomaly_type="PROTOCOL_VIOLATION",
severity="high",
src_ip=src_ip, dst_ip=dst_ip,
unit_id=unit_id, func_code=func_code,
detail=f"Non-standard protocol ID {proto_id} (expected 0)",
mitre_technique="T0830 - Man in the Middle",
))
# Rule 6: Broadcast write (unit ID 0)
if unit_id == 0 and func_code in (5, 6, 15, 16):
self.anomalies.append(ModbusAnomaly(
timestamp=ts_str,
anomaly_type="BROADCAST_WRITE",
severity="critical",
src_ip=src_ip, dst_ip=dst_ip,
unit_id=unit_id, func_code=func_code,
detail="Broadcast write command (unit ID 0) affects ALL slaves",
mitre_technique="T0855 - Unauthorized Command Message",
))
session.last_timestamp = timestamp
def analyze_pcap(self, pcap_file):
"""Analyze pcap file for Modbus anomalies."""
print(f"[*] Analyzing {pcap_file}...")
packets = rdpcap(pcap_file)
for pkt in packets:
self.process_packet(pkt)
print(f"[*] Processed {self.packet_count} Modbus packets")
def generate_report(self):
"""Generate anomaly detection report."""
print(f"\n{'='*70}")
print("MODBUS PROTOCOL ANOMALY DETECTION REPORT")
print(f"{'='*70}")
print(f"Packets analyzed: {self.packet_count}")
print(f"Sessions tracked: {len(self.sessions)}")
print(f"Anomalies detected: {len(self.anomalies)}")
severity_counts = defaultdict(int)
type_counts = defaultdict(int)
for a in self.anomalies:
severity_counts[a.severity] += 1
type_counts[a.anomaly_type] += 1
print(f"\nBy Severity:")
for sev in ["critical", "high", "medium", "low"]:
if severity_counts[sev]:
print(f" {sev.upper()}: {severity_counts[sev]}")
print(f"\nBy Type:")
for atype, count in sorted(type_counts.items(), key=lambda x: -x[1]):
print(f" {atype}: {count}")
print(f"\nTop Anomalies:")
for a in self.anomalies[:15]:
print(f" [{a.severity.upper()}] {a.anomaly_type}: {a.detail}")
if __name__ == "__main__":
detector = ModbusAnomalyDetector()
if len(sys.argv) > 1:
# Load baseline if provided
if len(sys.argv) > 2:
detector.load_baseline(sys.argv[2])
detector.analyze_pcap(sys.argv[1])
detector.generate_report()
else:
print("Usage: python modbus_detector.py <pcap_file> [baseline.json]")
Key Concepts
| Term | Definition |
|---|---|
| Modbus/TCP | Industrial protocol running on TCP port 502, consisting of an MBAP header and PDU with function code and data |
| Function Code | Modbus command identifier (FC1-4: reads, FC5-6/15-16: writes, FC8: diagnostics) determining the operation type |
| MBAP Header | Modbus Application Protocol header containing transaction ID, protocol ID (0x0000), length, and unit ID |
| Unit ID | Modbus address (0-247) identifying the target slave device; unit ID 0 is broadcast to all slaves |
| Register Map | Vendor-specific mapping of Modbus register addresses to process variables (e.g., register 40001 = reactor temperature) |
| Function Code Allowlist | Security policy defining which Modbus function codes are permitted from each source IP to each target device |
Tools & Systems
- Zeek Modbus Analyzer: Network security monitor with built-in Modbus/TCP protocol analysis and logging
- Suricata with ET Open ICS rules: IDS/IPS with Modbus-specific detection rules for command injection and anomalies
- Wireshark Modbus Dissector: Protocol analyzer with full Modbus/TCP and Modbus RTU decoding
- PyModbus: Python Modbus library for building custom monitoring and testing tools
Output Format
Modbus Protocol Anomaly Detection Report
==========================================
Capture Period: YYYY-MM-DD to YYYY-MM-DD
Packets Analyzed: [N]
Sessions: [N]
ANOMALIES: [N]
UNAUTHORIZED_CLIENT: [N]
UNAUTHORIZED_FUNCTION_CODE: [N]
WRITE_OPERATION: [N]
TIMING_ANOMALY: [N]
BROADCAST_WRITE: [N]
04 同仓库更多 Skills
verifying-build-provenance-with-slsa-sigstore
Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI/CD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.
validating-tpm-measured-boot-attestation
Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote/tpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware/kernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.
validating-backup-integrity-for-recovery
Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable/air-gapped backups for silent corruption or tampering.
triaging-windows-with-kape
Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment/triage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.
triaging-vulnerabilities-with-ssvc-framework
Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track/Track*/Attend/Act decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.
triaging-security-incident
Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM/EDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.