name: detecting-attacks-on-scada-systems description: 'This skill covers detecting cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) systems including man-in-the-middle attacks on industrial protocols, unauthorized command injection into PLCs, HMI compromise, historian data manipulation, and denial-of-service against control system communications. It leverages OT-specific intrusion detection systems, industrial protocol anomaly detection, and process data analytics to identify attacks that traditional IT security tools miss.

' domain: cybersecurity subdomain: ot-ics-security tags:

• ot-security
• ics
• scada
• industrial-control
• iec62443
• intrusion-detection
• threat-detection version: 1.0.0 author: mahipal license: Apache-2.0 nist_ai_rmf:
• MEASURE-2.7
• MAP-5.1
• MANAGE-2.4 atlas_techniques:
• AML.T0070
• AML.T0066
• AML.T0082 nist_csf:
• PR.IR-01
• DE.CM-01
• ID.AM-05
• GV.OC-02

Detecting Attacks on SCADA Systems

When to Use

• When deploying intrusion detection capabilities in a SCADA environment for the first time
• When investigating suspected cyber attacks against industrial control systems
• When building detection rules for OT-specific attack patterns (Stuxnet, TRITON, Industroyer)
• When integrating OT network monitoring with an enterprise SOC for unified threat visibility
• When responding to alerts from OT security monitoring tools (Dragos, Nozomi, Claroty)

Do not use for detecting attacks on IT-only networks without SCADA/ICS components, for building generic network IDS rules (see building-detection-rules-with-sigma), or for incident response procedures after an attack is confirmed (see performing-ot-incident-response).

Prerequisites

• Passive network monitoring sensors deployed on SPAN/TAP ports at OT network boundaries
• OT intrusion detection system (Dragos Platform, Nozomi Guardian, Claroty xDome, or Suricata with OT rulesets)
• Understanding of industrial protocols in use (Modbus, DNP3, OPC UA, EtherNet/IP, S7comm)
• Baseline of normal SCADA communication patterns (polling intervals, function codes, register ranges)
• Access to process historian data for physical process anomaly correlation

Workflow

Step 1: Establish SCADA Communication Baselines

Before detecting anomalies, establish what normal SCADA traffic looks like. Industrial protocols are highly deterministic - the same master polls the same slaves at the same intervals reading the same registers.

#!/usr/bin/env python3
"""SCADA Communication Baseline Builder.

Analyzes OT network traffic to establish deterministic baselines for
Modbus/TCP, DNP3, EtherNet/IP, and S7comm communications.
"""

import json
import sys
from collections import defaultdict
from datetime import datetime
from statistics import mean, stdev

try:
    from scapy.all import rdpcap, IP, TCP, UDP
except ImportError:
    print("Install scapy: pip install scapy")
    sys.exit(1)

MODBUS_FUNC_NAMES = {
    1: "Read Coils", 2: "Read Discrete Inputs",
    3: "Read Holding Registers", 4: "Read Input Registers",
    5: "Write Single Coil", 6: "Write Single Register",
    8: "Diagnostics", 15: "Write Multiple Coils",
    16: "Write Multiple Registers", 17: "Report Slave ID",
    22: "Mask Write Register", 23: "Read/Write Multiple Registers",
    43: "Encapsulated Interface Transport",
}


class SCADABaselineBuilder:
    """Builds deterministic baselines from SCADA traffic captures."""

    def __init__(self):
        self.modbus_sessions = defaultdict(lambda: {
            "func_codes": defaultdict(int),
            "register_ranges": set(),
            "intervals": [],
            "last_seen": None,
            "request_count": 0,
        })
        self.communication_pairs = defaultdict(lambda: {
            "protocols": set(),
            "packet_count": 0,
            "first_seen": None,
            "last_seen": None,
        })

    def process_pcap(self, pcap_file):
        """Process pcap file to build SCADA baselines."""
        packets = rdpcap(pcap_file)
        print(f"[*] Processing {len(packets)} packets for baseline...")

        for pkt in packets:
            if not pkt.haslayer(IP):
                continue

            src = pkt[IP].src
            dst = pkt[IP].dst
            ts = float(pkt.time)

            # Track communication pairs
            pair_key = f"{src}->{dst}"
            pair = self.communication_pairs[pair_key]
            pair["packet_count"] += 1
            if pair["first_seen"] is None:
                pair["first_seen"] = ts
            pair["last_seen"] = ts

            # Analyze Modbus/TCP
            if pkt.haslayer(TCP) and pkt[TCP].dport == 502:
                self._analyze_modbus(pkt, src, dst, ts)

    def _analyze_modbus(self, pkt, src, dst, timestamp):
        """Extract Modbus function codes and register ranges."""
        payload = bytes(pkt[TCP].payload)
        if len(payload) < 8:
            return

        # MBAP header: transaction_id(2) + protocol_id(2) + length(2) + unit_id(1) + func_code(1)
        func_code = payload[7]
        session_key = f"{src}->{dst}"
        session = self.modbus_sessions[session_key]

        session["func_codes"][func_code] += 1
        session["request_count"] += 1
        session["protocols"] = {"Modbus/TCP"}

        # Track polling intervals
        if session["last_seen"] is not None:
            interval = timestamp - session["last_seen"]
            if 0.01 < interval < 60:  # Reasonable polling interval
                session["intervals"].append(interval)
        session["last_seen"] = timestamp

        # Extract register range for read/write operations
        if len(payload) >= 12 and func_code in (1, 2, 3, 4, 5, 6, 15, 16):
            start_register = (payload[8] << 8) | payload[9]
            if func_code in (1, 2, 3, 4, 15, 16) and len(payload) >= 12:
                count = (payload[10] << 8) | payload[11]
                session["register_ranges"].add((func_code, start_register, start_register + count))

    def generate_baseline(self):
        """Generate the baseline profile from collected data."""
        baseline = {
            "generated": datetime.now().isoformat(),
            "modbus_baselines": {},
            "communication_pairs": {},
        }

        for session_key, session in self.modbus_sessions.items():
            avg_interval = mean(session["intervals"]) if session["intervals"] else 0
            interval_std = stdev(session["intervals"]) if len(session["intervals"]) > 1 else 0

            baseline["modbus_baselines"][session_key] = {
                "allowed_function_codes": list(session["func_codes"].keys()),
                "function_code_distribution": {
                    MODBUS_FUNC_NAMES.get(k, f"FC{k}"): v
                    for k, v in session["func_codes"].items()
                },
                "polling_interval_avg_sec": round(avg_interval, 3),
                "polling_interval_stddev": round(interval_std, 3),
                "register_ranges": [
                    {"func_code": r[0], "start": r[1], "end": r[2]}
                    for r in session["register_ranges"]
                ],
                "total_requests": session["request_count"],
            }

        return baseline

    def export_baseline(self, output_file):
        """Export baseline to JSON file."""
        baseline = self.generate_baseline()
        with open(output_file, "w") as f:
            json.dump(baseline, f, indent=2)
        print(f"[*] Baseline saved to: {output_file}")

        # Print summary
        print(f"\n{'='*60}")
        print("SCADA COMMUNICATION BASELINE SUMMARY")
        print(f"{'='*60}")
        for session, data in baseline["modbus_baselines"].items():
            print(f"\n  Session: {session}")
            print(f"    Function Codes: {data['allowed_function_codes']}")
            print(f"    Polling Interval: {data['polling_interval_avg_sec']}s (+/- {data['polling_interval_stddev']}s)")
            print(f"    Register Ranges: {len(data['register_ranges'])}")
            print(f"    Total Requests: {data['total_requests']}")


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: python scada_baseline.py <pcap_file> [output.json]")
        sys.exit(1)

    builder = SCADABaselineBuilder()
    builder.process_pcap(sys.argv[1])
    output = sys.argv[2] if len(sys.argv) > 2 else "scada_baseline.json"
    builder.export_baseline(output)

Step 2: Deploy OT-Specific Detection Rules

Create detection rules for known SCADA attack patterns including those used by TRITON, Industroyer/CrashOverride, and PIPEDREAM/INCONTROLLER.

# Suricata Rules for SCADA Attack Detection
# Deploy on IDS sensor monitoring OT network SPAN port

# --- Modbus Attack Detection ---

# Unauthorized Modbus write to PLC from non-engineering workstation
alert modbus any any -> $OT_PLC_SUBNET 502 (
  msg:"OT-DETECT Modbus write from unauthorized source";
  modbus_func:!read_coils; modbus_func:!read_discrete_inputs;
  modbus_func:!read_holding_registers; modbus_func:!read_input_registers;
  flow:to_server,established;
  threshold:type both, track by_src, count 1, seconds 60;
  classtype:attempted-admin;
  sid:3000001; rev:1;
)

# Modbus diagnostic/restart command (FC 8) - potential PLC DoS
alert modbus any any -> $OT_PLC_SUBNET 502 (
  msg:"OT-DETECT Modbus diagnostics command to PLC";
  modbus_func:diagnostics;
  flow:to_server,established;
  classtype:attempted-dos;
  sid:3000002; rev:1;
)

# Modbus broadcast write (unit ID 0) - affects all slaves
alert modbus any any -> $OT_PLC_SUBNET 502 (
  msg:"OT-CRITICAL Modbus broadcast write command";
  modbus_unit_id:0;
  flow:to_server,established;
  classtype:attempted-admin;
  sid:3000003; rev:1;
  priority:1;
)

# --- S7comm Attack Detection (Siemens) ---

# S7comm CPU STOP command - shuts down PLC execution
alert tcp any any -> $SIEMENS_PLC_SUBNET 102 (
  msg:"OT-CRITICAL S7comm CPU STOP command detected";
  content:"|03 00|"; offset:0; depth:2;
  content:"|29|"; offset:17; depth:1;
  flow:to_server,established;
  classtype:attempted-dos;
  sid:3000010; rev:1;
  priority:1;
)

# S7comm PLC program upload (potential logic modification)
alert tcp any any -> $SIEMENS_PLC_SUBNET 102 (
  msg:"OT-CRITICAL S7comm program download to PLC";
  content:"|03 00|"; offset:0; depth:2;
  content:"|1a|"; offset:17; depth:1;
  flow:to_server,established;
  classtype:attempted-admin;
  sid:3000011; rev:1;
  priority:1;
)

# --- DNP3 Attack Detection ---

# DNP3 cold restart command
alert tcp any any -> $OT_RTU_SUBNET 20000 (
  msg:"OT-CRITICAL DNP3 cold restart command";
  content:"|05 64|"; offset:0; depth:2;
  content:"|0d|"; offset:12; depth:1;
  flow:to_server,established;
  classtype:attempted-dos;
  sid:3000020; rev:1;
  priority:1;
)

# DNP3 firmware update command - potential PIPEDREAM indicator
alert tcp any any -> $OT_RTU_SUBNET 20000 (
  msg:"OT-CRITICAL DNP3 file transfer / firmware update";
  content:"|05 64|"; offset:0; depth:2;
  content:"|19|"; offset:12; depth:1;
  flow:to_server,established;
  classtype:attempted-admin;
  sid:3000021; rev:1;
  priority:1;
)

# --- Network Anomaly Detection ---

# New device communicating with PLCs (not in baseline)
alert ip !$AUTHORIZED_OT_HOSTS any -> $OT_PLC_SUBNET any (
  msg:"OT-DETECT Unauthorized device communicating with PLC subnet";
  flow:to_server;
  threshold:type limit, track by_src, count 1, seconds 3600;
  classtype:network-scan;
  sid:3000030; rev:1;
)

# Port scan targeting OT protocols
alert tcp any any -> $OT_NETWORK any (
  msg:"OT-DETECT Port scan targeting industrial protocols";
  flags:S;
  threshold:type threshold, track by_src, count 10, seconds 60;
  classtype:network-scan;
  sid:3000031; rev:1;
)

Step 3: Implement Process Data Anomaly Detection

Monitor physical process data from the historian to detect attacks that manipulate the process while hiding their effects from operators (the Stuxnet attack pattern).

#!/usr/bin/env python3
"""SCADA Process Data Anomaly Detector.

Monitors historian data to detect physical process anomalies
that may indicate cyber attacks manipulating control logic
while spoofing sensor readings (Stuxnet-style attacks).
"""

import json
import sys
import time
from collections import deque
from dataclasses import dataclass
from datetime import datetime
from statistics import mean, stdev
from typing import Optional

try:
    import requests
except ImportError:
    print("Install requests: pip install requests")
    sys.exit(1)


@dataclass
class ProcessVariable:
    """Represents a monitored process variable."""
    tag_name: str
    description: str
    unit: str
    low_limit: float
    high_limit: float
    rate_of_change_limit: float  # Maximum change per second
    engineering_low: float
    engineering_high: float


@dataclass
class Anomaly:
    """Represents a detected process anomaly."""
    timestamp: str
    tag_name: str
    anomaly_type: str
    severity: str
    current_value: float
    expected_range: str
    description: str
    attack_pattern: str = ""


class ProcessAnomalyDetector:
    """Detects anomalies in SCADA process data from historian."""

    def __init__(self, historian_url, api_key=None):
        self.historian_url = historian_url
        self.api_key = api_key
        self.variables = {}
        self.history = defaultdict(lambda: deque(maxlen=1000))
        self.anomalies = []

    def add_variable(self, var: ProcessVariable):
        """Register a process variable to monitor."""
        self.variables[var.tag_name] = var

    def fetch_current_values(self):
        """Fetch current values from historian API."""
        headers = {}
        if self.api_key:
            headers["Authorization"] = f"Bearer {self.api_key}"

        tag_list = list(self.variables.keys())
        params = {"tags": ",".join(tag_list), "count": 1}

        try:
            resp = requests.get(
                f"{self.historian_url}/api/v1/streams/values/current",
                params=params,
                headers=headers,
                timeout=10,
                verify=not os.environ.get("SKIP_TLS_VERIFY", "").lower() == "true",  # Set SKIP_TLS_VERIFY=true for self-signed certs in lab environments
            )
            resp.raise_for_status()
            return resp.json()
        except requests.RequestException as e:
            print(f"[ERROR] Historian API error: {e}")
            return {}

    def check_value(self, tag_name, value, timestamp):
        """Check a process variable value against all detection rules."""
        var = self.variables.get(tag_name)
        if not var:
            return

        self.history[tag_name].append((timestamp, value))

        # Rule 1: Value out of engineering limits
        if value < var.engineering_low or value > var.engineering_high:
            self.anomalies.append(Anomaly(
                timestamp=timestamp,
                tag_name=tag_name,
                anomaly_type="OUT_OF_RANGE",
                severity="critical",
                current_value=value,
                expected_range=f"{var.engineering_low}-{var.engineering_high} {var.unit}",
                description=f"{tag_name} ({var.description}) at {value} {var.unit} - outside engineering limits",
                attack_pattern="Process manipulation - value driven outside safe operating range",
            ))

        # Rule 2: Rate of change exceeds physical limits
        history = list(self.history[tag_name])
        if len(history) >= 2:
            prev_ts, prev_val = history[-2]
            try:
                dt = (datetime.fromisoformat(timestamp) - datetime.fromisoformat(prev_ts)).total_seconds()
                if dt > 0:
                    rate = abs(value - prev_val) / dt
                    if rate > var.rate_of_change_limit:
                        self.anomalies.append(Anomaly(
                            timestamp=timestamp,
                            tag_name=tag_name,
                            anomaly_type="RATE_OF_CHANGE_VIOLATION",
                            severity="high",
                            current_value=value,
                            expected_range=f"Max rate: {var.rate_of_change_limit} {var.unit}/s",
                            description=(
                                f"{tag_name} changing at {rate:.2f} {var.unit}/s "
                                f"(limit: {var.rate_of_change_limit} {var.unit}/s)"
                            ),
                            attack_pattern="Possible sensor spoofing or actuator manipulation",
                        ))
            except (ValueError, TypeError):
                pass

        # Rule 3: Flatline detection (sensor reading not changing when process is active)
        if len(history) >= 20:
            recent_values = [v for _, v in list(history)[-20:]]
            if len(set(recent_values)) == 1:
                self.anomalies.append(Anomaly(
                    timestamp=timestamp,
                    tag_name=tag_name,
                    anomaly_type="FLATLINE_DETECTED",
                    severity="high",
                    current_value=value,
                    expected_range="Expected variation during active process",
                    description=f"{tag_name} flatlined at {value} for 20+ consecutive readings",
                    attack_pattern="Stuxnet-style replay attack - frozen sensor value while process is manipulated",
                ))

        # Rule 4: Statistical anomaly (z-score based)
        if len(history) >= 50:
            values = [v for _, v in list(history)[-50:]]
            avg = mean(values)
            std = stdev(values) if len(values) > 1 else 0
            if std > 0:
                z_score = abs(value - avg) / std
                if z_score > 3.5:
                    self.anomalies.append(Anomaly(
                        timestamp=timestamp,
                        tag_name=tag_name,
                        anomaly_type="STATISTICAL_ANOMALY",
                        severity="medium",
                        current_value=value,
                        expected_range=f"Mean: {avg:.2f}, StdDev: {std:.2f} (z={z_score:.1f})",
                        description=f"{tag_name} value {value} is {z_score:.1f} standard deviations from mean",
                        attack_pattern="Possible gradual process manipulation",
                    ))

    def report_anomalies(self):
        """Print detected anomalies."""
        if not self.anomalies:
            print("[*] No anomalies detected")
            return

        print(f"\n{'='*70}")
        print(f"PROCESS ANOMALY DETECTION REPORT - {len(self.anomalies)} anomalies")
        print(f"{'='*70}")

        for a in self.anomalies:
            print(f"\n  [{a.severity.upper()}] {a.anomaly_type}")
            print(f"    Time: {a.timestamp}")
            print(f"    Tag: {a.tag_name}")
            print(f"    Value: {a.current_value}")
            print(f"    Expected: {a.expected_range}")
            print(f"    Detail: {a.description}")
            if a.attack_pattern:
                print(f"    Attack Pattern: {a.attack_pattern}")


if __name__ == "__main__":
    from collections import defaultdict

    detector = ProcessAnomalyDetector(
        historian_url="https://10.30.1.50:5450",
    )

    # Define monitored process variables for a chemical reactor
    detector.add_variable(ProcessVariable(
        tag_name="REACTOR_01.TEMP",
        description="Reactor 1 Temperature",
        unit="C",
        low_limit=150, high_limit=280,
        rate_of_change_limit=5.0,
        engineering_low=100, engineering_high=350,
    ))
    detector.add_variable(ProcessVariable(
        tag_name="REACTOR_01.PRESSURE",
        description="Reactor 1 Pressure",
        unit="bar",
        low_limit=2.0, high_limit=8.0,
        rate_of_change_limit=0.5,
        engineering_low=0, engineering_high=12.0,
    ))