What is a Process aware Attracks?
Process-aware attacks specifically target the physical processes controlled by Cyber-Physical Systems (CPS). They aim to disrupt the performance, efficiency, stability, or safety of the physical system by interfering with the feedback loop between the control logic and physical components. Unlike cyberattacks, which primarily exploit vulnerabilities in computing, communication, or networking layers, process-aware attacks exploit their understanding of the CPS's operational dynamics to cause precise and often devastating disruptions. Hence, how can process-aware attacks be distinguish from other kinds of cyber attacks?
Process-Aware Attacks vs Cyberattacks
Process-Aware Attacks: Example: Stuxnet: A process-aware attack on Iranian nuclear centrifuges. By modifying control signals, it caused the centrifuges to operate at unsafe speeds while feeding normal readings to monitoring systems, thereby evading detection. Attack Mechanism: Targets control logic, runtime parameters (e.g., controller gains), or sensor/actuator data (e.g., sensor spoofing). Impact: Directly affects the physical process, causing deviations in behavior that degrade performance or lead to failures.
Cyberattacks: Example: Distributed Denial-of-Service (DDoS): Overwhelms a network or server with traffic, rendering it inaccessible. Attack Mechanism: Exploits weaknesses in software, hardware, or network configurations. Impact: Primarily disrupts the availability of digital resources rather than physical operations.
Differences in Knowledge Required to Develop and Conduct the Process Attacks and CyberAttack
Process-Aware Attacks: Domain Knowledge: Requires an in-depth understanding of the physical system’s control mechanisms and operational dynamics. Example Requirement: Knowledge of a PLC’s programming, actuator limitations, or sensor configurations. Sophistication: High; attackers often simulate or study the target system to craft precise disruptions. Cyberattacks: Technical Knowledge: Requires expertise in network protocols, encryption, software vulnerabilities, etc. Example Requirement: Familiarity with operating systems or application-layer exploits. Sophistication: Varies; some attacks (e.g., phishing) require minimal technical knowledge, while others (e.g., zero-day exploits) demand advanced skills.
Primary Modes of Process-Aware Attacks on Distributed Control Systems
Process-aware attacks exploit vulnerabilities within feedback loops, communication channels, and sensor/actuator signals in a DCS. The primary effects include system instability, data compromise, and physical damage. Mitigating these effects requires a comprehensive, multilayered approach, combining secure communications, anomaly detection, redundancy, and robust monitoring to ensure system resilience.
1. Modification of Computational Node Behavior
Process-aware attacks on computational nodes involve altering their parameters or logic, often through firmware or runtime modifications. The primary effect is the disruption of the control system's ability to execute commands correctly, potentially leading to unsafe or inefficient operations. This type of attack can also generate corrupted or deceptive outputs, which can propagate false data across the system and cause widespread instability.
2. Modification of Messages between Nodes
Attacks on communication channels involve tampering with messages exchanged between computational nodes, such as altering, delaying, or blocking messages. This disrupts feedback loops and causes inconsistencies in system coordination. The primary effects include delayed or erroneous responses in control actions, destabilization of operations, and potential cascading failures in interconnected systems.
3. Modification of Sensor or Actuator Signals
These attacks target the integrity of sensor and actuator data by tampering with signals or firmware. For example, spoofing sensor readings can provide controllers with misleading data, leading to incorrect control actions. The primary effects include undetected process deviations, unsafe operating conditions, and potential damage to the system or its components.
Interdependence of Process-Aware Attacks
These attack types are inherently interconnected. For example, an attack on computational node behavior can alter outgoing messages, causing downstream nodes to interpret the changes as sensor spoofing. Similarly, modifying or dropping communication messages can mimic sensor signal manipulation, as nodes may receive incomplete or altered data. This interconnectedness complicates detection and mitigation efforts.
Primary Effects and Resiliency Strategies
The primary effects of these attacks include disrupted control logic, destabilized feedback loops, compromised data integrity, and widespread operational inefficiencies. To mitigate such effects, a robust distributed control system must incorporate multilayered attack monitoring approaches. This involves validating incoming messages, monitoring outgoing commands, and safeguarding communication links. Air-gapped monitoring nodes, analog fallback mechanisms (e.g., alarms or relays), and separate communication networks for control and monitoring nodes further enhance system resiliency, making it harder for attackers to infiltrate multiple layers.
Primary Mechanisms for Monitoring a Cyber-Physical System for Process-Aware Attacks
Controller-Focused Attack Monitor (CFAM) - CFAM monitors real-time behavior of the controller as a dynamic mapping from sensor signals (input) to actuator signals (output). Any deviation in the expected control algorithm or controller dynamics is flagged as a potential attack. Hence, the purpose is for detect modifications to the controller or unexpected controller dynamics. System-Focused Attack Monitor (SFAM) - The SFAM monitors real-time system behavior as a dynamic mapping from actuator signals (input) to sensor signals (output). It identifies deviations from the expected process dynamics. In fact, this purpose is to detect attacks on sensors or actuators that alter the dynamic behavior of the system. Additional Auxiliary Signals for Monitoring – refers to the redundant sensors or monitoring devices that provide additional data about the dynamic process. These could measure environmental conditions, additional system variables, or analog emissions (e.g., electromagnetic signals) from system components. Moreover, it provides robustness and aid in distinguishing between normal system uncertainties and attack-related changes.
Equipment Used for Detection and Mitigation
Sensors
Sensors, such as redundant and process-specific devices like pressure or temperature sensors, are vital for monitoring critical process variables. They continuously analyze data to detect anomalies, providing early warning of attacks or malfunctions.
Actuators
Actuators, including pneumatic, hydraulic, or electronic types with monitoring features, ensure accurate execution of commands. By verifying that signals match expected dynamics, they help detect tampering or unauthorized changes.
Computational Nodes
Industrial PCs, PLCs, and SCADA systems act as computational nodes that implement attack detection algorithms like CFAM and SFAM. These nodes analyze system behavior in real-time, enabling quick detection of deviations from expected performance.
Communication Protocol Analyzers
Communication protocol analyzers, such as Modbus analyzers or Ethernet/IP sniffers, monitor data transmission for inconsistencies. They detect irregularities or unauthorized modifications in communication, protecting the integrity of the system.
Redundant Systems
Redundant systems, including backup sensors, controllers, and communication paths, provide an extra layer of security by cross-verifying primary data. This redundancy improves reliability and ensures anomalies caused by attacks or failures are quickly identified.
Analog Emission Monitors
Analog emission monitors, like electromagnetic or acoustic sensors, detect unusual emissions from devices. These anomalies may indicate tampering, device malfunction, or potential cyber-physical attacks.
Real-Time Monitoring Software
Real-time monitoring software, such as custom CFAM and SFAM applications or SCADA solutions, provides detailed visualization of system behavior. These tools enable rapid identification of deviations, improving response times during potential attacks.
Attack Mitigation Systems
Attack mitigation systems, including firewalls, intrusion detection systems (IDS), and safety interlocks, protect the system during an attack. They isolate compromised components to maintain operational safety and prevent further damage. By integrating CFAM, SFAM, and auxiliary monitoring, a robust process-aware monitoring system can detect and mitigate cyber-physical attacks effectively. Image credit: Reversinglabs
Source: F. Khorrami, P. Krishnamurthy and R. Karri, “Cybersecurity for Control Systems: A Process-Aware Perspective,” in IEEE Design & Test, vol. 33, no. 5, pp. 75-83, Oct. 2016, doi: 10.1109/MDAT.2016.2594178
