detecting-stuxnet-style-attacks

This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process manipulation.

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最近来源活动: 2026年3月16日 00:59
检测到的 SKILL.md 语言: 英语
星标: 10
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name	detecting-stuxnet-style-attacks
description	This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process manipulation.
domain	cybersecurity
subdomain	ot-ics-security
tags	["ot-security","ics","scada","industrial-control","iec62443","stuxnet","plc-integrity","apt"]
version	1.0.0
author	mahipal
license	Apache-2.0

Detecting Stuxnet-Style Attacks

When to Use

When implementing advanced threat detection for high-value OT targets (nuclear, chemical, critical infrastructure)
When building detection for APT-style attacks targeting PLC logic and process manipulation
When establishing PLC logic integrity monitoring to detect unauthorized modifications
When investigating suspected process anomalies that may indicate cyber-physical attacks
When designing defense-in-depth strategies against nation-state level OT threats

Do not use for basic OT intrusion detection (see detecting-attacks-on-scada-systems), for malware analysis of Stuxnet samples (see malware reverse engineering skills), or for PLC programming and logic development.

Prerequisites

Detailed understanding of the Stuxnet attack chain and MITRE ATT&CK for ICS framework
PLC logic backup repository with known-good baseline copies of all PLC programs
Engineering workstation monitoring (EDR with OT awareness)
Physics-based process models for the controlled physical process
Network monitoring for industrial protocol traffic analysis

Workflow

Step 1: Understand the Stuxnet Attack Chain

Map detection opportunities across the multi-stage Stuxnet-style attack chain.

# Stuxnet-Style Attack Chain and Detection Points
attack_chain:
  stage_1_initial_access:
    technique: "USB-borne malware targeting air-gapped network"
    mitre_ics: "T0847 - Replication Through Removable Media"
    detection:
      - "USB device connection logging on engineering workstations"
      - "Removable media scanning with OT-approved AV"
      - "Application allowlisting blocking unauthorized executables"
      - "Windows autorun disabled via Group Policy"
    indicators:
      - "New USB device connections to engineering workstations"
      - "Execution of unsigned binaries from removable media"

#!/usr/bin/env python3
"""PLC Logic Integrity Monitor.

Periodically retrieves PLC program block information and compares
against known-good baselines to detect unauthorized modifications
(Stuxnet-style logic injection).
"""

import hashlib
import json
import sys
import time
from dataclasses import dataclass, field, asdict
from datetime import datetime


@dataclass
class PLCBlock:
    """Represents a PLC program block."""
    block_type: str  # OB, FC, FB, DB
    block_number: int
    name: str
    size_bytes: int
    checksum: str
    last_modified: str
    author: str = ""


@dataclass
class IntegrityAlert:
    alert_id: str
    timestamp: str
    severity: str
    plc_name: str
    plc_ip: str
    alert_type: str
    description: str
    baseline_value: str
    current_value: str
    mitre_technique: str


class PLCIntegrityMonitor:
    """Monitors PLC program integrity against baselines."""

    def __init__(self):
        self.baselines = {}  # plc_name -> list of PLCBlock
        self.alerts = []
        self.alert_counter = 1

    def load_baseline(self, plc_name, baseline_file):
        """Load known-good PLC program baseline."""
        with open(baseline_file) as f:
            data = json.load(f)
        blocks = [PLCBlock(**b) for b in data.get("blocks", [])]
        self.baselines[plc_name] = {
            "blocks": {f"{b.block_type}{b.block_number}": b for b in blocks},
            "total_blocks": len(blocks),
            "loaded_at": datetime.now().isoformat(),
        }
        print(f"[*] Loaded baseline for {plc_name}: {len(blocks)} blocks")

    def check_integrity(self, plc_name, plc_ip, current_blocks):
        """Compare current PLC program against baseline."""
        baseline = self.baselines.get(plc_name)
        if not baseline:
            print(f"[WARN] No baseline for {plc_name}")
            return

        baseline_blocks = baseline["blocks"]
        current_block_map = {f"{b.block_type}{b.block_number}": b for b in current_blocks}

        # Check 1: New blocks added (potential logic injection)
        for key, block in current_block_map.items():
            if key not in baseline_blocks:
                self.alerts.append(IntegrityAlert(
                    alert_id=f"INT-{self.alert_counter:04d}",
                    timestamp=datetime.now().isoformat(),
                    severity="critical",
                    plc_name=plc_name,
                    plc_ip=plc_ip,
                    alert_type="NEW_BLOCK_DETECTED",
                    description=(
                        f"New program block {key} ({block.name}) found in PLC "
                        f"that does not exist in baseline. Size: {block.size_bytes} bytes."
                    ),
                    baseline_value="Block does not exist in baseline",
                    current_value=f"{key}: {block.size_bytes} bytes, checksum {block.checksum}",
                    mitre_technique="T0839 - Module Firmware / T0833 - Modify Control Logic",
                ))
                self.alert_counter += 1

        # Check 2: Blocks removed
        for key in baseline_blocks:
            if key not in current_block_map:
                self.alerts.append(IntegrityAlert(
                    alert_id=f"INT-{self.alert_counter:04d}",
                    timestamp=datetime.now().isoformat(),
                    severity="high",
                    plc_name=plc_name,
                    plc_ip=plc_ip,
                    alert_type="BLOCK_REMOVED",
                    description=f"Program block {key} removed from PLC",
                    baseline_value=f"{key}: {baseline_blocks[key].size_bytes} bytes",
                    current_value="Block not found",
                    mitre_technique="T0833 - Modify Control Logic",
                ))
                self.alert_counter += 1

        # Check 3: Block content modified (checksum mismatch)
        for key in baseline_blocks:
            if key in current_block_map:
                baseline_block = baseline_blocks[key]
                current_block = current_block_map[key]

                if baseline_block.checksum != current_block.checksum:
                    self.alerts.append(IntegrityAlert(
                        alert_id=f"INT-{self.alert_counter:04d}",
                        timestamp=datetime.now().isoformat(),
                        severity="critical",
                        plc_name=plc_name,
                        plc_ip=plc_ip,
                        alert_type="BLOCK_MODIFIED",
                        description=(
                            f"Program block {key} checksum mismatch. "
                            f"Logic has been modified since baseline was established."
                        ),
                        baseline_value=f"Checksum: {baseline_block.checksum}, Size: {baseline_block.size_bytes}",
                        current_value=f"Checksum: {current_block.checksum}, Size: {current_block.size_bytes}",
                        mitre_technique="T0833 - Modify Control Logic",
                    ))
                    self.alert_counter += 1

        # Check 4: Block count change
        if len(current_blocks) != baseline["total_blocks"]:
            self.alerts.append(IntegrityAlert(
                alert_id=f"INT-{self.alert_counter:04d}",
                timestamp=datetime.now().isoformat(),
                severity="high",
                plc_name=plc_name,
                plc_ip=plc_ip,
                alert_type="BLOCK_COUNT_CHANGE",
                description=f"Total block count changed from {baseline['total_blocks']} to {len(current_blocks)}",
                baseline_value=str(baseline["total_blocks"]),
                current_value=str(len(current_blocks)),
                mitre_technique="T0833 - Modify Control Logic",
            ))
            self.alert_counter += 1

    def generate_report(self):
        """Generate integrity monitoring report."""
        print(f"\n{'='*70}")
        print("PLC LOGIC INTEGRITY MONITORING REPORT")
        print(f"{'='*70}")
        print(f"Baselines loaded: {len(self.baselines)}")
        print(f"Alerts: {len(self.alerts)}")

        for a in self.alerts:
            print(f"\n  [{a.severity.upper()}] {a.alert_type}")
            print(f"    PLC: {a.plc_name} ({a.plc_ip})")
            print(f"    {a.description}")
            print(f"    Baseline: {a.baseline_value}")
            print(f"    Current: {a.current_value}")
            print(f"    MITRE: {a.mitre_technique}")


if __name__ == "__main__":
    monitor = PLCIntegrityMonitor()
    print("PLC Logic Integrity Monitor")
    print("Load baselines and call check_integrity() periodically")

#!/usr/bin/env python3
"""Physics-Based Cyber-Physical Attack Detector.

Uses simplified physics models to detect process manipulation
attacks where the attacker modifies the physical process while
spoofing sensor readings (the core Stuxnet attack pattern).
"""

import math
from dataclasses import dataclass
from datetime import datetime


@dataclass
class PhysicsAlert:
    timestamp: str
    severity: str
    alert_type: str
    sensor_tag: str
    reported_value: float
    predicted_value: float
    deviation_percent: float
    description: str


class CentrifugePhysicsModel:
    """Physics model for a centrifuge system (Stuxnet target analog).

    Detects manipulation by cross-correlating:
    - Motor frequency (Hz) vs reported RPM
    - RPM vs vibration signature
    - Power consumption vs rotational speed
    """

    def __init__(self, rated_rpm=1200, rated_frequency=50, rated_power_kw=75):
        self.rated_rpm = rated_rpm
        self.rated_frequency = rated_frequency
        self.rated_power_kw = rated_power_kw
        self.alerts = []

    def check_frequency_rpm_correlation(self, frequency_hz, reported_rpm):
        """Verify motor frequency matches reported RPM.

        For an induction motor: RPM = 120 * frequency / poles
        If RPM is being spoofed, it won't match the actual frequency.
        """
        # Assuming 4-pole motor with typical 3% slip
        expected_rpm = (120 * frequency_hz / 4) * 0.97
        deviation = abs(reported_rpm - expected_rpm) / expected_rpm * 100

        if deviation > 5.0:
            self.alerts.append(PhysicsAlert(
                timestamp=datetime.now().isoformat(),
                severity="critical",
                alert_type="FREQUENCY_RPM_MISMATCH",
                sensor_tag="MOTOR.RPM vs VFD.FREQ",
                reported_value=reported_rpm,
                predicted_value=round(expected_rpm, 1),
                deviation_percent=round(deviation, 1),
                description=(
                    f"Motor RPM ({reported_rpm}) does not match VFD frequency "
                    f"({frequency_hz} Hz). Expected ~{expected_rpm:.0f} RPM. "
                    f"Possible RPM sensor spoofing while frequency is manipulated."
                ),
            ))

    def check_power_speed_correlation(self, rpm, power_kw):
        """Verify power consumption matches rotational speed.

        Power scales approximately with RPM^3 for centrifugal loads.
        """
        speed_ratio = rpm / self.rated_rpm
        expected_power = self.rated_power_kw * (speed_ratio ** 3)
        deviation = abs(power_kw - expected_power) / max(expected_power, 0.1) * 100

        if deviation > 15.0:
            self.alerts.append(PhysicsAlert(
                timestamp=datetime.now().isoformat(),
                severity="high",
                alert_type="POWER_SPEED_MISMATCH",
                sensor_tag="MOTOR.POWER vs MOTOR.RPM",
                reported_value=power_kw,
                predicted_value=round(expected_power, 1),
                deviation_percent=round(deviation, 1),
                description=(
                    f"Power consumption ({power_kw} kW) inconsistent with RPM ({rpm}). "
                    f"Expected ~{expected_power:.1f} kW. May indicate hidden speed changes."
                ),
            ))

    def check_vibration_anomaly(self, rpm, vibration_mm_s):
        """Check if vibration signature is consistent with operating speed.

        Abnormal vibration at reported 'normal' speed may indicate actual
        speed is different from what sensors report.
        """
        # Normal vibration increases linearly with speed for balanced rotor
        speed_ratio = rpm / self.rated_rpm
        expected_vibration = 2.0 * speed_ratio  # mm/s baseline
        deviation = abs(vibration_mm_s - expected_vibration) / max(expected_vibration, 0.1) * 100

        if vibration_mm_s > 7.0:  # ISO 10816 alert threshold
            self.alerts.append(PhysicsAlert(
                timestamp=datetime.now().isoformat(),
                severity="critical",
                alert_type="ABNORMAL_VIBRATION",
                sensor_tag="MOTOR.VIBRATION",
                reported_value=vibration_mm_s,
                predicted_value=round(expected_vibration, 1),
                deviation_percent=round(deviation, 1),
                description=(
                    f"Vibration ({vibration_mm_s} mm/s) at ISO alert level while "
                    f"RPM reports normal ({rpm}). Actual speed may differ from reported."
                ),
            ))

    def report(self):
        if self.alerts:
            print(f"\n{'='*60}")
            print("PHYSICS-BASED ANOMALY DETECTION ALERTS")
            print(f"{'='*60}")
            for a in self.alerts:
                print(f"\n  [{a.severity.upper()}] {a.alert_type}")
                print(f"    {a.description}")
                print(f"    Reported: {a.reported_value} | Predicted: {a.predicted_value}")
                print(f"    Deviation: {a.deviation_percent}%")


if __name__ == "__main__":
    model = CentrifugePhysicsModel(rated_rpm=1200, rated_frequency=50, rated_power_kw=75)

    # Normal operation - no alerts expected
    model.check_frequency_rpm_correlation(50.0, 1164)
    model.check_power_speed_correlation(1164, 72.0)

    # Stuxnet-style attack: frequency increased but RPM spoofed as normal
    model.check_frequency_rpm_correlation(84.0, 1164)  # freq up, RPM spoofed
    model.check_power_speed_correlation(1164, 180.0)    # power reveals true speed

    model.report()

Term	Definition
Cyber-Physical Attack	Attack that manipulates both the cyber system (PLC logic, sensor readings) and the physical process simultaneously
Logic Injection	Inserting malicious code blocks into PLC programs to alter physical process behavior
Sensor Spoofing	Replaying or fabricating sensor readings to hide process manipulation from operators
Physics-Based Detection	Using mathematical models of physical processes to detect when reported sensor values are inconsistent with actual physics
PLC Logic Baseline	Known-good copy of PLC program blocks (OB, FC, FB, DB) used for integrity comparison
Air-Gap Bridging	Technique of crossing air-gapped networks via USB drives, as used by Stuxnet's initial access method

Stuxnet-Style Attack Detection Report
========================================
Monitored PLCs: [N]
Monitoring Period: YYYY-MM-DD to YYYY-MM-DD

PLC INTEGRITY:
  Baselines verified: [N]/[N]
  Logic modifications detected: [N]
  New blocks detected: [N]

PHYSICS ANOMALIES:
  Sensor correlation violations: [N]
  Process model deviations: [N]

ENGINEERING WORKSTATION:
  Unauthorized modifications: [N]
  USB connections: [N]