# An RL-Based Adaptive Detection Strategy to Secure Cyber-Physical Systems

arXiv: https://arxiv.org/abs/2103.02872 · HTML: https://mlsanity.com/paper/2103.02872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Cyber-Physical Systems Security -- A Survey](https://mlsanity.com/paper/1701.04525/) (2017)
- [Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems](https://mlsanity.com/paper/1805.00983/) (2018)
- [Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2008.06162/) (2020)
- [Integrating Security in Resource-Constrained Cyber-Physical Systems](https://mlsanity.com/paper/1811.03538/) (2018)
- [Optimal Thresholds for Anomaly-Based Intrusion Detection in Dynamical Environments](https://mlsanity.com/paper/1606.06707/) (2016)
- [Formal Synthesis of Monitoring and Detection Systems for Secure CPS Implementations](https://mlsanity.com/paper/2002.12412/) (2020)

## Abstract (arXiv)

Increased dependence on networked, software based control has escalated the vulnerabilities of Cyber Physical Systems (CPSs). Detection and monitoring components developed leveraging dynamical systems theory are often employed as lightweight security measures for protecting such safety critical CPSs against false data injection attacks. However, existing approaches do not correlate attack scenarios with parameters of detection systems. In the present work, we propose a Reinforcement Learning (RL) based framework which adaptively sets the parameters of such detectors based on experience learned from attack scenarios, maximizing detection rate and minimizing false alarms in the process while attempting performance preserving control actions.