Reinforcement Learning-Based Approaches for Enhancing Security and Resilience in Smart Control: A Survey on Attack and Defense Methods

• URL: http://arxiv.org/abs/2402.15617v1
• Date: Fri, 23 Feb 2024 21:48:50 GMT
• Title: Reinforcement Learning-Based Approaches for Enhancing Security and Resilience in Smart Control: A Survey on Attack and Defense Methods
• Authors: Zheyu Zhang,
• Abstract summary: Reinforcement Learning (RL) learns to make decisions based on real-world experiences. This paper reviews the latest adversarial RL threats and outlines effective defense strategies tailored to safeguard these applications. By concentrating on the smart grid and smart home scenarios, this survey equips ML developers and researchers with the insights needed to secure RL applications.
• Score: 0.3626013617212667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning (RL), one of the core paradigms in machine learning, learns to make decisions based on real-world experiences. This approach has significantly advanced AI applications across various domains, notably in smart grid optimization and smart home automation. However, the proliferation of RL in these critical sectors has also exposed them to sophisticated adversarial attacks that target the underlying neural network policies, compromising system integrity. Given the pivotal role of RL in enhancing the efficiency and sustainability of smart grids and the personalized convenience in smart homes, ensuring the security of these systems is paramount. This paper aims to bolster the resilience of RL frameworks within these specific contexts, addressing the unique challenges posed by the intricate and potentially adversarial environments of smart grids and smart homes. We provide a thorough review of the latest adversarial RL threats and outline effective defense strategies tailored to safeguard these applications. Our comparative analysis sheds light on the nuances of adversarial tactics against RL-driven smart systems and evaluates the defense mechanisms, focusing on their innovative contributions, limitations, and the compromises they entail. By concentrating on the smart grid and smart home scenarios, this survey equips ML developers and researchers with the insights needed to secure RL applications against emerging threats, ensuring their reliability and safety in our increasingly connected world.

Related papers

• A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities [4.589028594967462]
  We provide an in-depth analysis of various attack vectors, focusing on new attack surfaces introduced by advanced components in smart grids. Following this, we examine innovative detection and mitigation strategies, including game theory, graph theory, and machine learning. We first discuss the research opportunities for existing and emerging strategies, and then explore the potential role of new techniques.
  arXiv  Detail & Related papers  (2024-07-10T18:03:24Z)
• GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
  We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
  arXiv  Detail & Related papers  (2024-05-20T14:43:46Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies [0.8785883427835897]
  This work evaluates several reinforcement learning approaches implemented in the second edition of the CAGE Challenge. We find that the ensemble RL technique performs strongest, outperforming our other models and taking second place in the competition. In unseen environments, all of our approaches perform worse, with varied degradation based on the type of environmental change.
  arXiv  Detail & Related papers  (2022-11-28T17:01:24Z)
• Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training [71.7750435554693]
  We show that several state-of-the-art RL agents proposed for power system control are vulnerable to adversarial attacks. Specifically, we use an adversary Markov Decision Process to learn an attack policy, and demonstrate the potency of our attack. We propose to use adversarial training to increase the robustness of RL agent against attacks and avoid infeasible operational decisions.
  arXiv  Detail & Related papers  (2021-10-18T00:50:34Z)
• Constraints Satisfiability Driven Reinforcement Learning for Autonomous Cyber Defense [7.321728608775741]
  We present a new hybrid autonomous agent architecture that aims to optimize and verify defense policies of reinforcement learning (RL) We use constraints verification (using satisfiability modulo theory (SMT)) to steer the RL decision-making toward safe and effective actions. Our evaluation of the presented approach in a simulated CPS environment shows that the agent learns the optimal policy fast and defeats diversified attack strategies in 99% cases.
  arXiv  Detail & Related papers  (2021-04-19T01:08:30Z)
• Blockchained Federated Learning for Threat Defense [0.0]
  This research paper introduces the development of an intelligent Threat Defense system, employing Federated Learning. The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms. The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods.
  arXiv  Detail & Related papers  (2021-02-25T09:16:48Z)
• Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning [91.13113161754022]
  We introduce timing-based adversarial strategies against a DRL-based navigation system by jamming in physical noise patterns on the selected time frames. Our experimental results show that the adversarial timing attacks can lead to a significant performance drop.
  arXiv  Detail & Related papers  (2020-02-20T21:39:25Z)
• Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning [48.49658986576776]
  Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications. This paper presents emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks.
  arXiv  Detail & Related papers  (2020-01-27T10:53:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.