Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques

TL;DR

This paper reviews various anomaly detection techniques in Cyber-Physical Systems, categorizing methods like data-driven, model-driven, hybrid, and system-oriented approaches to improve security and reliability.

Contribution

It provides a comprehensive categorization and comparison of anomaly detection methods in CPS, highlighting strengths, weaknesses, and research gaps.

Findings

Data-driven methods like machine learning are effective but require large datasets.

Model-driven approaches offer interpretability but may lack adaptability.

Hybrid methods like Physics-Informed Neural Networks combine strengths of both.

Abstract

In an increasingly interconnected world, Cyber-Physical Systems (CPS) are essential to critical industries like healthcare, transportation, and manufacturing, merging physical processes with computational intelligence. However, the security of these systems is a major concern. Anomalies, whether from sensor malfunctions or cyberattacks, can lead to catastrophic failures, making effective detection vital for preventing harm and service disruptions. This paper provides a comprehensive review of anomaly detection techniques in CPS. We categorize and compare various methods, including data-driven approaches (machine learning, deep learning, machine learning-deep learning ensemble), model-driven approaches (mathematical, invariant-based), hybrid datamodel approaches (Physics-Informed Neural Networks), and system-oriented approaches. Our analysis highlights the strengths and weaknesses of each technique, offering a practical guide for creating safer and more reliable systems. By identifying current research gaps, we aim to inspire future work that will enhance the security and adaptability of CPS in our automated world.