Imported from mukul975/anthropic-cybersecurity-skills (skills/monitoring-scada-modbus-traffic-anomalies/SKILL.md). Install upstream with npx skills add mukul975/anthropic-cybersecurity-skills --skill monitoring-scada-modbus-traffic-anomalies. Copyright stays with the author (Apache-2.0).

Monitoring SCADA Modbus Traffic Anomalies

When to Use

• Monitoring OT/ICS networks for unauthorized Modbus commands targeting PLCs, RTUs, or HMIs
• Detecting reconnaissance activity such as Modbus device enumeration (function code 43, Read Device Identification)
• Identifying unauthorized write operations (function codes 05, 06, 15, 16) to coils and holding registers that could alter physical process parameters
• Baselining normal Modbus communication patterns and alerting on deviations in function code distribution, register access ranges, or timing intervals
• Investigating suspected sabotage or insider threats manipulating SCADA process values through Modbus register writes

Do not use on networks without authorization from the asset owner, for active injection or fuzzing against production SCADA systems, or as a replacement for safety-instrumented systems (SIS) that provide physical process protection.

Prerequisites

• Network tap or SPAN port on the OT network segment carrying Modbus TCP traffic (port 502)
• Python 3.9+ with pymodbus (>=3.6), scapy (>=2.5), and pandas for traffic analysis
• Zeek (formerly Bro) installed with the Modbus protocol analyzer enabled for passive traffic logging
• Wireshark or tshark for initial packet capture and validation of Modbus frame structure
• A baseline period of normal operations (minimum 48-72 hours) to establish communication profiles per device pair
• Network diagram identifying Modbus master-slave relationships, device IP addresses, and expected function code usage

Workflow

Step 1: Capture and Parse Modbus TCP Traffic

Establish passive monitoring on the OT network segment and begin capturing Modbus TCP frames:

• Configure network tap: Position the monitoring interface on the SPAN port mirroring the VLAN carrying Modbus TCP traffic between HMI/SCADA servers and PLCs. Verify bidirectional traffic capture with tcpdump -i eth0 port 502 -c 100 -w modbus_capture.pcap.
• Parse Modbus TCP frame structure: Each Modbus TCP frame contains a 7-byte MBAP (Modbus Application Protocol) header followed by the PDU. The MBAP header includes:
  • Transaction Identifier (2 bytes): Matches requests to responses
  • Protocol Identifier (2 bytes): Always 0x0000 for Modbus
  • Length (2 bytes): Number of following bytes including Unit ID
  • Unit Identifier (1 byte): Slave device address (0-247)
• Extract function codes with Scapy: Use Scapy's Modbus contrib module to dissect captured packets and extract function codes, register addresses, and values:

  from scapy.all import rdpcap, TCP
  from scapy.contrib.modbus import ModbusADURequest, ModbusADUResponse
  
  packets = rdpcap("modbus_capture.pcap")
  for pkt in packets:
      if pkt.haslayer(ModbusADURequest):
          adu = pkt[ModbusADURequest]
          print(f"Src: {pkt['IP'].src} -> Dst: {pkt['IP'].dst} "
                f"Unit: {adu.unitId} FuncCode: {adu.funcCode}")

• Enable Zeek Modbus logging: Configure Zeek with @load policy/protocols/modbus/known-masters-slaves to generate modbus.log entries containing timestamp, source/destination IPs, function code, and exception responses. This provides continuous passive logging without custom scripting.
• Validate frame integrity: Check for malformed Modbus frames where the MBAP length field does not match the actual PDU length, Protocol Identifier is not 0x0000, or Unit Identifier falls outside the expected range for the monitored network.

Step 2: Baseline Normal Communication Patterns

Build a behavioral profile of legitimate Modbus traffic to distinguish normal operations from anomalies:

• Catalog function code distribution: Record the frequency of each function code per source-destination pair over the baseline period. In typical SCADA environments, read operations (FC 01-04) vastly outnumber write operations (FC 05, 06, 15, 16), often at ratios exceeding 100:1. A sudden increase in write function codes is a strong indicator of process manipulation.

  Normal baseline example (72-hour period):
  HMI (10.1.1.10) -> PLC (10.1.1.50):
    FC 03 (Read Holding Registers):  432,180 packets  (97.2%)
    FC 04 (Read Input Registers):     10,540 packets  (2.4%)
    FC 06 (Write Single Register):     1,780 packets  (0.4%)
    FC 16 (Write Multiple Registers):      0 packets  (0.0%)
    FC 43 (Read Device ID):               0 packets  (0.0%)
  

• Map register address ranges: Document which holding register and coil address ranges each master polls. PLCs typically expose specific register blocks for monitoring (e.g., registers 0-99 for process values, 100-199 for setpoints). Access to registers outside the documented range indicates reconnaissance or misconfiguration.
• Establish timing profiles: Calculate the polling interval (mean, standard deviation) for each master-slave pair. SCADA polling is highly periodic, typically 100ms to 5s intervals. Deviations greater than 3 standard deviations from the mean suggest network issues or injected traffic from a rogue master.
• Identify authorized masters: Record all IP addresses that initiate Modbus requests (master role). In a properly segmented OT network, only the HMI server and engineering workstation should act as Modbus masters. Any new source IP sending Modbus requests is immediately suspicious.
• Register value ranges: For critical process registers (temperatures, pressures, flow rates, setpoints), record the observed minimum, maximum, mean, and standard deviation during normal operations. Values outside the physical process bounds indicate either sensor failure or malicious manipulation.

Step 3: Detect Function Code Anomalies

Apply rule-based and statistical detection to identify suspicious function code usage:

• Unauthorized write detection: Alert when a Modbus write function code (05, 06, 15, 16) originates from a source IP not in the authorized writers list, or when write operations exceed the baseline frequency threshold:

  WRITE_FUNCTION_CODES = {5, 6, 15, 16}
  AUTHORIZED_WRITERS = {"10.1.1.10", "10.1.1.11"}  # HMI and engineering WS
  
  def check_unauthorized_write(src_ip, function_code):
      if function_code in WRITE_FUNCTION_CODES and src_ip not in AUTHORIZED_WRITERS:
          return {
              "alert": "UNAUTHORIZED_MODBUS_WRITE",
              "severity": "CRITICAL",
              "src_ip": src_ip,
              "function_code": function_code,
              "description": f"Write FC {function_code} from unauthorized source {src_ip}"
          }
      return None

• Reconnaissance detection: Function code 43 (Read Device Identification) and function code 08 (Diagnostics) are rarely used during normal operations. Any occurrence from a non-engineering workstation indicates device enumeration. Also detect sequential scanning where a single source queries multiple Unit IDs within a short window.
• Exception response monitoring: Modbus exception codes (01: Illegal Function, 02: Illegal Data Address, 03: Illegal Data Value) in responses indicate the master sent an invalid request. A burst of exception responses suggests fuzzing or protocol-level attacks:

  Exception response correlation:
  - Isolated exception (1-2 per hour): Normal operational error
  - Burst (>10 per minute): Active scanning or fuzzing attempt
  - Continuous (>100 per hour): Denial-of-service or tool malfunction
  

• Forbidden function code detection: Some environments prohibit certain function codes entirely. Function codes 07 (Read Exception Status), 08 (Diagnostics), 17 (Report Slave ID), and 43 (Read Device Identification) are diagnostic functions that should not appear in production SCADA traffic. Alert on any occurrence.
• Function code frequency anomaly: Calculate the chi-squared statistic comparing the observed function code distribution against the baseline distribution. A significant deviation (p < 0.01) triggers an alert even if no individual function code crosses its threshold.

Step 4: Monitor Register Values for Process Manipulation

Detect attempts to manipulate physical process parameters through register value analysis:

• Setpoint change monitoring: Track all write operations to holding registers that control process setpoints (temperatures, pressures, valve positions, motor speeds). Alert when:
  • The new value exceeds the defined safe operating range
  • The rate of change exceeds physical process capabilities (e.g., temperature setpoint jumping 50 degrees in one write)
  • Multiple setpoints change simultaneously, which does not match normal operator behavior

  REGISTER_LIMITS = {
      40001: {"name": "Reactor Temperature Setpoint", "min": 50, "max": 200, "unit": "C",
              "max_rate": 5},   # Max 5 degrees per write cycle
      40010: {"name": "Pump Speed", "min": 0, "max": 3600, "unit": "RPM",
              "max_rate": 200},  # Max 200 RPM change per cycle
      40020: {"name": "Valve Position", "min": 0, "max": 100, "unit": "%",
              "max_rate": 10},   # Max 10% per cycle
  }
  
  def check_register_value(register_addr, new_value, previous_value):
      if register_addr not in REGISTER_LIMITS:
          return None
      limits = REGISTER_LIMITS[register_addr]
      alerts = []
      if new_value < limits["min"] or new_value > limits["max"]:
          alerts.append({
              "alert": "REGISTER_VALUE_OUT_OF_RANGE",
              "severity": "CRITICAL",
              "register": register_addr,
              "name": limits["name"],
              "value": new_value,
              "range": f"{limits['min']}-{limits['max']} {limits['unit']}"
          })
      if previous_value is not None:
          rate = abs(new_value - previous_value)
          if rate > limits["max_rate"]:
              alerts.append({
                  "alert": "REGISTER_VALUE_EXCESSIVE_RATE",
                  "severity": "HIGH",
                  "register": register_addr,
                  "name": limits["name"],
                  "change": rate,
                  "max_allowed": limits["max_rate"]
              })
      return alerts if alerts else None

• Coil state monitoring: Track coil writes (FC 05, FC 15) that control discrete outputs (pumps on/off, valves open/close, breakers trip/close). Detect rapid toggling (more than N state changes per minute) which could indicate equipment damage attempts.
• Register read pattern anomaly: If a master begins reading register ranges it has never accessed before, this may indicate an attacker using a compromised HMI to map the PLC memory layout before launching a targeted write attack.
• Correlation with process data: Where available, compare Modbus register values against independent process sensors (e.g., historian data). Discrepancies between the Modbus-reported value and the independent sensor indicate either sensor spoofing or register manipulation.

Step 5: Detect Network-Level Anomalies

Identify anomalies in communication patterns that may indicate man-in-the-middle, replay, or denial-of-service attacks:

• Rogue master detection: Alert when a new source IP initiates Modbus TCP connections to port 502 on any slave device. Maintain a whitelist of authorized master IPs and generate a critical alert for any connection from an unknown source:

  AUTHORIZED_MASTERS = {"10.1.1.10", "10.1.1.11"}
  
  def detect_rogue_master(src_ip, dst_ip, dst_port):
      if dst_port == 502 and src_ip not in AUTHORIZED_MASTERS:
          return {
              "alert": "ROGUE_MODBUS_MASTER",
              "severity": "CRITICAL",
              "src_ip": src_ip,
              "target_slave": dst_ip,
              "description": "Unauthorized device initiating Modbus connection"
          }
      return None

• Transaction ID anomaly: Modbus TCP uses transaction IDs to match requests with responses. Under normal operation, transaction IDs increment sequentially per master. Detect:
  • Duplicate transaction IDs from different sources (replay attack indicator)
  • Transaction ID gaps or resets (session hijacking indicator)
  • Responses with transaction IDs that do not match any recent request (injected response)
• Timing anomaly detection: Calculate inter-packet arrival times for each master-slave pair. Flag deviations greater than 3 standard deviations using a sliding window:

  import numpy as np
  from collections import defaultdict
  
  class TimingAnomalyDetector:
      def __init__(self, window_size=1000, threshold_sigma=3.0):
          self.windows = defaultdict(list)
          self.window_size = window_size
          self.threshold_sigma = threshold_sigma
  
      def check(self, src_ip, dst_ip, timestamp):
          key = (src_ip, dst_ip)
          window = self.windows[key]
          if len(window) > 0:
              interval = timestamp - window[-1]
              if len(window) >= 100:
                  mean = np.mean(np.diff(window[-100:]))
                  std = np.std(np.diff(window[-100:]))
                  if std > 0 and abs(interval - mean) > self.threshold_sigma * std:
                      return {
                          "alert": "TIMING_ANOMALY",
                          "severity": "MEDIUM",
                          "pair": f"{src_ip}->{dst_ip}",
                          "interval": interval,
                          "expected_mean": mean,
                          "deviation_sigma": abs(interval - mean) / std
                      }
          window.append(timestamp)
          if len(window) > self.window_size:
              window.pop(0)
          return None

• Connection flood detection: Monitor the rate of new TCP connections to port 502 per slave device. Modbus slaves typically handle 1-5 persistent connections. More than 10 connection attempts per minute to a single slave indicates a connection flood DoS or scanning activity.
• Payload size anomaly: Modbus PDU max size is 253 bytes. Alert on oversized frames that exceed protocol limits, as these may indicate buffer overflow exploitation attempts against vulnerable PLC firmware.

Key Concepts

Tools & Systems

• pymodbus: Python library for Modbus protocol implementation supporting TCP, RTU, and ASCII modes. Used for building custom Modbus clients/servers, packet parsing, and simulating master-slave communication in test environments.
• Scapy (contrib.modbus): Packet manipulation framework with Modbus TCP dissector for crafting, parsing, and sniffing Modbus frames. Enables field-level access to MBAP headers, function codes, and register data in captured packets.
• Zeek (formerly Bro): Network security monitor with native Modbus protocol analyzer that generates structured logs (modbus.log) for every Modbus transaction including function codes, register addresses, and exception responses.
• Wireshark/tshark: Network protocol analyzer with built-in Modbus TCP dissector for visual inspection of packet captures, filtering by function code (modbus.func_code == 6), and exporting specific fields for analysis.
• GRFICSv2: An open-source virtual ICS environment for security research featuring a simulated chemical process with Modbus-connected PLCs, HMI, and historian. Used for testing detection rules against realistic SCADA traffic.
• Suricata: Network IDS/IPS with Modbus protocol support via application-layer rules that can match on function codes, register addresses, and values for real-time alerting.

Common Scenarios

Scenario: Detecting Unauthorized Parameter Manipulation in a Water Treatment Plant

Context: A water treatment facility uses Modbus TCP to communicate between the SCADA server (10.1.1.10) and six PLCs controlling chemical dosing pumps, filtration valves, and flow meters. The security team deploys passive Modbus traffic monitoring after an industry advisory about attacks targeting water utilities.

Approach:

1. Deploy a network tap on the OT VLAN switch mirroring all port 502 traffic to the monitoring interface. Run Zeek with Modbus logging and the custom Python analyzer in parallel.
2. Establish a 72-hour baseline during normal operations, cataloging function code distribution, register access patterns, and polling intervals for all six master-slave pairs.
3. Baseline reveals the SCADA server only uses FC 03 (Read Holding Registers) and FC 06 (Write Single Register) to PLC-3 (chemical dosing), with writes occurring 2-4 times per day matching operator shift changes.
4. On day 5, the analyzer detects FC 16 (Write Multiple Registers) from 10.1.1.10 to PLC-3, a function code never seen in the baseline. The write targets registers 40050-40055, which control chlorine dosing rates.
5. Seconds later, a second alert fires: the chlorine dosing setpoint in register 40050 changed from 2.5 mg/L to 25.0 mg/L, exceeding the safe maximum of 4.0 mg/L defined in the register value limits.
6. Cross-referencing with IT network logs reveals the SCADA server was accessed via Remote Desktop from an unauthorized VPN connection 20 minutes before the anomalous Modbus traffic.
7. The operations team is notified, the chemical dosing PLC is placed in manual override, and the incident response team isolates the compromised SCADA server.

Pitfalls:

• Relying solely on IT-side network monitoring (firewall logs, IDS) that does not inspect Modbus application-layer content and would see only a normal TCP connection on port 502
• Not defining per-register safe operating ranges, which would miss the dangerous dosing rate change despite detecting the unusual function code
• Setting the baseline period too short (e.g., 4 hours) and missing legitimate but infrequent write operations that occur only during shift changes or maintenance windows
• Failing to correlate OT network anomalies with IT network events, missing the RDP session that was the actual attack vector

Scenario: Identifying Modbus Device Enumeration from a Compromised Engineering Workstation

Context: A manufacturing plant's SOC observes unusual network activity from an engineering workstation (10.1.2.20) that is authorized for PLC programming. The OT security team uses Modbus traffic monitoring to determine if the workstation is being used for reconnaissance.

Approach:

1. Filter Modbus traffic logs for all activity from 10.1.2.20 over the past 24 hours and compare against the baseline communication profile for that workstation.
2. Baseline shows 10.1.2.20 communicates with PLC-1 (10.1.1.50) only during scheduled maintenance windows using FC 03 and FC 06, approximately 200 packets per session.
3. Anomaly detection identifies 10.1.2.20 sent FC 43 (Read Device Identification) to 15 different IP addresses on the OT VLAN within a 10-minute window, none of which it has previously communicated with.
4. Further analysis shows FC 03 read requests to register ranges 0-9999 in blocks of 125 registers per request, systematically mapping the entire register space of each PLC contacted.
5. The engineering workstation is isolated, forensic imaging initiated, and all Modbus communication from that IP is blocked at the OT firewall. The device identification responses captured reveal the PLC firmware versions that the attacker obtained.

Pitfalls:

• Not flagging the engineering workstation because it is in the authorized masters list, missing that its communication pattern deviated drastically from its baseline profile
• Not detecting sequential register scanning because each individual read request is a valid FC 03 operation; only the aggregate pattern reveals the reconnaissance
• Blocking the workstation before capturing forensic evidence of the attack scope and exfiltrated data

Output Format

## Modbus Traffic Anomaly Report

**Monitoring Period**: 2026-03-15 00:00:00 UTC to 2026-03-15 23:59:59 UTC
**Network Segment**: OT VLAN 10 (10.1.1.0/24)
**Packets Analyzed**: 2,847,320
**Anomalies Detected**: 4

---

### Alert 1: Unauthorized Write Operation

**Timestamp**: 2026-03-15 14:23:17 UTC
**Severity**: CRITICAL
**Source**: 10.1.2.20 (Engineering Workstation)
**Destination**: 10.1.1.52 (PLC-3 Chemical Dosing)
**Function Code**: 16 (Write Multiple Registers)
**Registers**: 40050-40055
**Values Written**: [250, 100, 0, 1, 3600, 1]
**Baseline**: FC 16 never observed for this source-destination pair

**Context**: Register 40050 (Chlorine Dosing Rate) changed from 25 to 250
(safe range: 10-40). Register 40054 (Dosing Timer) changed from 1800 to 3600.
Combined effect would double chlorine concentration over extended period.

**Recommended Action**: Immediately verify physical process state. Isolate
source device. Check register values against expected setpoints with
plant operator.

---

### Alert 2: Device Enumeration Detected

**Timestamp**: 2026-03-15 14:20:05 to 14:20:47 UTC
**Severity**: HIGH
**Source**: 10.1.2.20
**Targets**: 10.1.1.50, 10.1.1.51, 10.1.1.52, 10.1.1.53, 10.1.1.54 (+10 more)
**Function Code**: 43 (Read Device Identification)
**Baseline**: FC 43 never observed from this source

**Context**: Sequential scanning of 15 devices in 42 seconds. Device
identification responses reveal PLC vendor, model, and firmware versions
for all scanned devices.

**Recommended Action**: Investigate source workstation for compromise
indicators. Block FC 43 from non-engineering subnets at OT firewall.