mlsanityData to 7 Oct 2026

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

arXiv 2102.07244 ↗First posted 14 Feb 2021cs.CR cs.AI cs.LGcited by 10

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Abstract (arXiv)

Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 99 of its 191 references that are in the map. First posted on arXiv 14 Feb 2021.

Builds on

99 of 191 references.

References · 99

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38 more
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50 most cited of 99 All 99 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 4; 2026: 1.

10 papers.

  1. 2022A Survey on Physical Adversarial Attack in Computer Visioncited by 24
  2. 2023VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformercited by 10
  3. 2025Worst-case generation via minimax optimization in Wasserstein spacecited by 7
  4. 2022Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systemscited by 5
  5. 2024Filtered Randomized Smoothing: A New Defense for Robust Modulation Classificationcited by 3
  6. 2024Adaptive Anomaly Detection for Identifying Attacks in Cyber-Physical Systems: A Systematic Literature Reviewcited by 2
  7. 2025TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learningcited by 2
  8. 2025The Use of the Simplex Architecture to Enhance Safety in Deep-Learning-Powered Autonomous Systemscited by 2
  9. 2025RockNet: Distributed Learning on Ultra-Low-Power Devicescited by 1
  10. 2026Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systemscited by 0