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

arXiv: https://arxiv.org/abs/2502.13256 · HTML: https://mlsanity.com/paper/2502.13256/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 226 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 30 of 33)

- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Graph Neural Network-Based Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2106.06947/) (2021)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT](https://mlsanity.com/paper/2104.03466/) (2021)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence](https://mlsanity.com/paper/1909.00560/) (2019)
- [Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering](https://mlsanity.com/paper/2106.07992/) (2021)
- [Cyber-Physical Systems Security -- A Survey](https://mlsanity.com/paper/1701.04525/) (2017)
- [Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review](https://mlsanity.com/paper/2010.14946/) (2020)
- [IoT Anomaly Detection Methods and Applications: A Survey](https://mlsanity.com/paper/2207.09092/) (2022)
- [Cybersecurity for Industrial Control Systems: A Survey](https://mlsanity.com/paper/2002.04124/) (2020)
- [Cybersecurity of Industrial Cyber-Physical Systems: A Review](https://mlsanity.com/paper/2101.03564/) (2021)
- [A Survey on Industrial Control System Testbeds and Datasets for Security Research](https://mlsanity.com/paper/2102.05631/) (2021)
- [Real-Time Sensor Anomaly Detection and Recovery in Connected Automated Vehicle Sensors](https://mlsanity.com/paper/1911.01531/) (2019)
- [A Survey on Security and Privacy Issues in Modern Healthcare Systems: Attacks and Defenses](https://mlsanity.com/paper/2005.07359/) (2020)
- [Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System](https://mlsanity.com/paper/1801.00903/) (2018)
- [Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems](https://mlsanity.com/paper/2204.09183/) (2022)
- [MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network](https://mlsanity.com/paper/2311.02378/) (2023)
- [Real-Time Bus Arrival Prediction: A Deep Learning Approach for Enhanced Urban Mobility](https://mlsanity.com/paper/2303.15495/) (2023)
- [Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation](https://mlsanity.com/paper/1609.01491/) (2016)
- [Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles](https://mlsanity.com/paper/1907.00511/) (2019)
- [ICSSIM-A Framework for Building Industrial Control Systems Security Simulation Testbeds](https://mlsanity.com/paper/2210.13325/) (2022)
- [Checking is Believing: Event-Aware Program Anomaly Detection in Cyber-Physical Systems](https://mlsanity.com/paper/1805.00074/) (2018)
- [An Overview of Cyber Threats, Attacks, and Countermeasures on the Primary Domains of Smart Cities](https://mlsanity.com/paper/2207.04424/) (2022)

## Built on by (most cited first, 4 of 4)

- [Reasoning with LLMs for Zero-Shot Vulnerability Detection](https://mlsanity.com/paper/2503.17885/) (2025)
- [Shopformer: Transformer-Based Framework for Detecting Shoplifting via Human Pose](https://mlsanity.com/paper/2504.19970/) (2025)
- [Machine-Learning Driven Load Shedding to Mitigate Instability Attacks in Power Grids](https://mlsanity.com/paper/2509.26532/) (2025)
- [Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approach](https://mlsanity.com/paper/2511.12351/) (2025)

## Abstract (arXiv)

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.