# Cyber-Physical Systems Security -- A Survey

arXiv: https://arxiv.org/abs/1701.04525 · HTML: https://mlsanity.com/paper/1701.04525/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2017.

## Built on by (most cited first, 19 of 19)

- [Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks](https://mlsanity.com/paper/2502.13175/) (2025)
- [Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering](https://mlsanity.com/paper/2106.07992/) (2021)
- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [IoT-Enabled Social Relationships Meet Artificial Social Intelligence](https://mlsanity.com/paper/2103.01776/) (2021)
- [What Breaks Embodied AI Security:LLM Vulnerabilities, CPS Flaws,or Something Else?](https://mlsanity.com/paper/2602.17345/) (2026)
- [MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network](https://mlsanity.com/paper/2311.02378/) (2023)
- [SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning](https://mlsanity.com/paper/2101.02780/) (2021)
- [Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques](https://mlsanity.com/paper/2502.13256/) (2025)
- [Adaptive Anomaly Detection for Identifying Attacks in Cyber-Physical Systems: A Systematic Literature Review](https://mlsanity.com/paper/2411.14278/) (2024)
- [Decentralized Multi-Agent Swarms for Autonomous Grid Security in Industrial IoT: A Consensus-based Approach](https://mlsanity.com/paper/2601.17303/) (2026)
- [Cross-Layered Distributed Data-driven Framework For Enhanced Smart Grid Cyber-Physical Security](https://mlsanity.com/paper/2111.05460/) (2021)
- [AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation](https://mlsanity.com/paper/2606.08173/) (2026)
- [Diff-DDoS: Realistic Cyber-Physical Attack Synthesis and Robust Detection for 5G-Enabled CPS Using Tabular Diffusion Models](https://mlsanity.com/paper/2608.17796/) (2026)
- [An RL-Based Adaptive Detection Strategy to Secure Cyber-Physical Systems](https://mlsanity.com/paper/2103.02872/) (2021)
- [Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations](https://mlsanity.com/paper/2202.10387/) (2022)
- [Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning](https://mlsanity.com/paper/2302.10601/) (2023)
- [LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models](https://mlsanity.com/paper/2409.00340/) (2024)
- [Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox](https://mlsanity.com/paper/2506.20102/) (2025)
- [MulRobBench: A Decision-Level Benchmark for Safe and Security-Policy-Compliant Multimodal UAV Agents](https://mlsanity.com/paper/2607.23870/) (2026)

## Abstract (arXiv)

With the exponential growth of cyber-physical systems (CPS), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lack a systematic study of CPS security issues. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it very difficult to study the problem with one generalized model. In this paper, we capture and systematize existing research on CPS security under a unified framework. The framework consists of three orthogonal coordinates: (1) from the security perspective, we follow the well-known taxonomy of threats, vulnerabilities, attacks and controls; (2)from the CPS components perspective, we focus on cyber, physical, and cyber-physical components; and (3) from the CPS systems perspective, we explore general CPS features as well as representative systems (e.g., smart grids, medical CPS and smart cars). The model can be both abstract to show general interactions of a CPS application and specific to capture any details when needed. By doing so, we aim to build a model that is abstract enough to be applicable to various heterogeneous CPS applications; and to gain a modular view of the tightly coupled CPS components. Such abstract decoupling makes it possible to gain a systematic understanding of CPS security, and to highlight the potential sources of attacks and ways of protection.