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detecting-modbus-protocol-anomalies

Lets your agent detect anomalies in Modbus industrial traffic. Monitors function codes, register validations, and communication patterns to identify suspicious activity.

What it does

This tool helps find unusual activity in Modbus industrial communication traffic. It checks if the commands used are expected and if the data accessed is within normal ranges.

When to use it

  • ✓When setting up a security system for industrial networks.
  • ✓When investigating a problem or suspicious activity on a production line.
  • ✓When creating a record of normal network behavior for future comparison.

Example of what to ask your AI

“Check Modbus traffic for unauthorized commands or unusual data access.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/skills/detecting-modbus-protocol-anomalies/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/mukul975/Anthropic-Cybersecurity-Skills/blob/main/skills/detecting-modbus-protocol-anomalies/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The tool monitors the commands (function codes) and data (registers) exchanged on the network. It compares current traffic against a record of what's considered normal to detect deviations.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-modbus-protocol-anomalies

Or download just the file:

curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/detecting-modbus-protocol-anomalies/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

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):

…
FAQ

What is Modbus?

Modbus is a communication protocol used in industrial systems for devices like sensors and controllers to talk to each other.

What are 'anomalies' in this context?

Anomalies are activities that deviate from the normal pattern, such as using disallowed commands or accessing unexpected data areas, which could indicate a problem or an attack.

Do I need technical knowledge to use this?

Some familiarity with industrial networks and the Modbus protocol is recommended to interpret the results, but the tool is designed to be as clear as possible.

Details

Original description by the author

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 t

repo: mukul975/Anthropic-Cybersecurity-Skills

Popularity

GitHub stars
35k
Ranking position
#5688
License
Apache-2.0
Updated
8/31/2026

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