Modular Safety Filter for Cyber-Physical Systems

Core Concepts

Introducing a modular safety filter to ensure plant safety against cyber attacks in Cyber-Physical Systems.

Abstract

The content discusses the need for solutions to protect control systems from cyber attacks in networked Cyber-Physical Systems. It introduces a modular safety filter approach that can effectively handle various types of cyber attacks while maintaining system safety and performance. The paper outlines the architecture, problem statement, and proposed safety filter algorithm. It also presents simulation results demonstrating the effectiveness of the safety filter during intelligent and false data injection attacks on a multi-agent mobile robot system. I. Introduction: Control systems are vulnerable to cyber attacks due to network integration. Solutions are needed to safeguard systems against potential cyber threats. II. Problem Statement: Describes the physical system and types of cyber attacks considered. Defines admissible sets, safe sets, and adversarial capabilities. III. Modular Safety Filter: Proposes an architecture for implementing a safety filter in CPS. Outlines the optimization problem for finding safe backup trajectories. IV. Multi-Agent Mobile Robot System: Discusses the application of the safety filter in a multi-agent mobile robot setup. Defines safety constraints and formation tasks for robots. V. Numerical Results: Presents simulation results for intelligent and FDI attacks on the mobile robot system. Demonstrates how the safety filter prevents collisions and maintains system safety. VI. Conclusions and Future Works: Highlights the effectiveness of the proposed modular safety filter. Discusses future work on adapting the filter for decentralized scenarios and addressing conservatism issues.

Stats

The attacker may use previous inputs and states. Due to its modularity, this method can be used as a standalone technology alongside other resilient controllers and anomaly detectors. A circular reference trajectory with constant radius, r0, and constant angular velocity, ω0, is defined as the formation task for the multi-agent mobile robot system.

Quotes

"The proposed architecture aims to address gaps in separating control performance from safety." "A predictive-based safety filter approximates safe backup trajectories toward final safe sets." "The proposed solution allows for both high performance during normal operation and safety during an attack."

Key Insights Distilled From

A Modular Safety Filter for Safety-Certified Cyber-Physical Systems

by Mohammad Baj... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15854.pdf

Deeper Inquiries

How can conservatism introduced by safety filters impact anomaly detectors in CPS?

The conservatism introduced by safety filters in Cyber-Physical Systems (CPS) can have a significant impact on anomaly detectors. When safety filters err on the side of caution and introduce unnecessary conservatism, it may lead to false positives being triggered by anomaly detectors. This occurs because the safety filter's overly cautious approach might flag normal system behavior as anomalous due to its conservative nature. Anomaly detectors are designed to identify deviations from expected or normal system behavior, signaling potential threats or attacks. However, when working in conjunction with a safety filter that introduces conservatism, the anomaly detector may interpret these cautious actions as anomalies themselves. As a result, there is an increased risk of false alarms and unnecessary interventions triggered by the anomaly detection system. To mitigate this issue, it is essential for designers to strike a balance between ensuring system safety through conservative measures while also considering the implications for anomaly detection systems. Fine-tuning the parameters of both the safety filter and the anomaly detector to align their thresholds appropriately can help reduce false positives and ensure efficient threat detection without compromising system performance.

How do alternative methods like Control Barrier Functions compare to predictive filters in ensuring system safety?

Control Barrier Functions (CBFs) offer an alternative method for ensuring system safety in CPS compared to predictive filters like those proposed in the context above. While predictive filters focus on predicting safe trajectories based on optimization problems and constraints, CBFs take a different approach by providing guarantees of forward-invariance of safe sets under control inputs. CBFs work by defining barrier functions that act as constraints on the state space such that if these barriers are not violated during controller execution, then certain properties such as stability or collision avoidance are guaranteed. In contrast, predictive filters anticipate unsafe situations before they occur through optimization-based trajectory planning towards safe regions. One key difference lies in their implementation: CBFs provide formal guarantees using Lyapunov-like analysis techniques while predictive filters rely on solving optimization problems iteratively at runtime. Additionally, CBFs are often used for decentralized control scenarios where each agent enforces its own barrier function locally without explicit knowledge of other agents' states or objectives. In summary, while both approaches aim to ensure system safety in CPS applications, CBFs offer formal guarantees based on barrier functions that prevent unsafe states from being reached directly during control execution. On the other hand, predictive filters proactively plan safe trajectories based on predicted future states but may introduce computational overhead due to iterative optimization processes.

What adaptations are needed to implement a decentralized version of the proposed modular safety filter?

Adapting the proposed modular safety filter for decentralized implementation involves several key modifications: Communication Protocols: Implementing communication protocols that enable agents within a decentralized network to exchange relevant information such as state estimates and predicted trajectories. Neighbor Interaction: Agents need mechanisms to interact with neighboring agents autonomously without centralized coordination. Decentralized Decision-Making: Each agent should be capable of making decisions independently based on local information while adhering to global objectives defined by shared goals or constraints. Consensus Algorithms: Incorporating consensus algorithms allows agents within a networked environment to converge towards common decisions despite having limited information about other agents' states. 5Distributed Optimization: Enabling distributed optimization techniques ensures that each agent can compute its optimal input trajectory considering local constraints and objectives while collaborating with others efficiently. 6Fault Tolerance Mechanisms: Implementing fault tolerance mechanisms becomes crucial since decentralization increases vulnerability; therefore robust error-handling strategies must be integrated into each agent's decision-making process. These adaptations facilitate seamless integration of individual autonomous entities into a cohesive decentralized framework governed by shared principles outlined within collaborative tasks or mission requirements within Cyber-Physical Systems settings