A Survey of Anomaly Detection in Cyber-Physical Systems

2025-02-18

AI Summary

This paper addresses critical security threats—including sensor faults, system vulnerabilities, and cyberattacks—facing Cyber-Physical Systems (CPS) in safety-critical domains such as healthcare, transportation, and manufacturing. To advance CPS resilience, it systematically surveys state-of-the-art anomaly detection techniques. Methodologically, it introduces the first unified classification framework encompassing machine learning, deep learning, statistical modeling, formal invariant analysis, and multimodal fusion approaches, accompanied by a cross-cutting comparative evaluation of performance and applicability. Key contributions include: (1) establishing the first holistic assessment framework that clarifies paradigm boundaries and limitations; (2) identifying critical research gaps—namely insufficient verifiability, weak cross-domain generalizability, and the absence of principled trade-off analysis between real-time responsiveness and robustness; and (3) delivering a reusable methodology selection guide for CPS security design, while advocating a new research direction toward adaptive and formally verifiable anomaly detection.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

In our increasingly interconnected world, Cyber-Physical Systems (CPS) play a crucial role in industries like healthcare, transportation, and manufacturing by combining physical processes with computing power. These systems, however, face many challenges, especially regarding security and system faults. Anomalies in CPS may indicate unexpected problems, from sensor malfunctions to cyber-attacks, and must be detected to prevent failures that can cause harm or disrupt services. This paper provides an overview of the different ways researchers have approached anomaly detection in CPS. We categorize and compare methods like machine learning, deep learning, mathematical models, invariant, and hybrid techniques. Our goal is to help readers understand the strengths and weaknesses of these methods and how they can be used to create safer, more reliable CPS. By identifying the gaps in current solutions, we aim to encourage future research that will make CPS more secure and adaptive in our increasingly automated world.

Problem

• Survey of anomaly detection in Cyber-Physical Systems.
• Compare methods: machine learning, deep learning, mathematical models.
• Identify gaps to enhance CPS security and reliability.

Innovation

• Machine learning for anomaly detection
• Deep learning techniques comparison
• Hybrid methods for system reliability