Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges

• URL: http://arxiv.org/abs/2208.13885v1
• Date: Mon, 29 Aug 2022 20:57:35 GMT
• Title: Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges
• Authors: Satwik Patnaik, Vasudev Gohil, Hao Guo, Jeyavijayan (JV) Rajendran
• Abstract summary: Reinforcement learning (RL) is a machine learning paradigm where an autonomous agent learns to make an optimal sequence of decisions. This brief outlines the development of RL agents in detecting hardware Trojans, one of the most challenging hardware security problems.
• Score: 6.87143729255904
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reinforcement learning (RL) is a machine learning paradigm where an autonomous agent learns to make an optimal sequence of decisions by interacting with the underlying environment. The promise demonstrated by RL-guided workflows in unraveling electronic design automation problems has encouraged hardware security researchers to utilize autonomous RL agents in solving domain-specific problems. From the perspective of hardware security, such autonomous agents are appealing as they can generate optimal actions in an unknown adversarial environment. On the other hand, the continued globalization of the integrated circuit supply chain has forced chip fabrication to off-shore, untrustworthy entities, leading to increased concerns about the security of the hardware. Furthermore, the unknown adversarial environment and increasing design complexity make it challenging for defenders to detect subtle modifications made by attackers (a.k.a. hardware Trojans). In this brief, we outline the development of RL agents in detecting hardware Trojans, one of the most challenging hardware security problems. Additionally, we outline potential opportunities and enlist the challenges of applying RL to solve hardware security problems.

Related papers

• CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus [45.24207460381396]
  This paper presents a novel Intrusion Detection System (IDS) designed for the Controller Area Network (CAN) environment.<n>A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks.<n>Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.
  arXiv  Detail & Related papers  (2025-07-19T20:09:52Z)
• Securing Generative AI Agentic Workflows: Risks, Mitigation, and a Proposed Firewall Architecture [0.0]
  Generative Artificial Intelligence (GenAI) presents significant advancements but also introduces novel security challenges.<n>This paper outlines critical security vulnerabilities inherent in GenAI agentic, including data privacy, model manipulation, and issues related to agent autonomy and system integration.<n>It details a proposed "GenAI Security Firewall" architecture designed to provide comprehensive, adaptable, and efficient protection for these systems.
  arXiv  Detail & Related papers  (2025-06-10T07:36:54Z)
• Transformers for Secure Hardware Systems: Applications, Challenges, and Outlook [2.9625426098772425]
  Transformer models have gained traction in the security domain due to their ability to model complex dependencies.<n>This survey provides a review of recent advancements on the use of Transformers in hardware security.<n>It examines their application across key areas such as side-channel analysis, hardware Trojan detection, vulnerability classification, device fingerprinting, and firmware security.
  arXiv  Detail & Related papers  (2025-05-28T17:22:14Z)
• Hardware-Enabled Mechanisms for Verifying Responsible AI Development [17.536212903072105]
  Hardware-enabled mechanisms (HEMs) can support responsible AI development by enabling verifiable reporting of key properties of AI training activities.<n>Such tools can promote transparency and improve security, while addressing privacy and intellectual property concerns.
  arXiv  Detail & Related papers  (2025-04-02T22:23:39Z)
• Runtime Detection of Adversarial Attacks in AI Accelerators Using Performance Counters [5.097354139604596]
  We propose SAMURAI, a novel framework for safeguarding against malicious usage of AI hardware. SAMURAI introduces an AI Performance Counter ( APC) for tracking dynamic behavior of an AI model. APC records the runtime profile of the low-level hardware events of different AI operations. The summary information recorded by the APC is processed by TANTO to efficiently identify potential security breaches.
  arXiv  Detail & Related papers  (2025-03-10T17:38:42Z)
• Enhancing Enterprise Security with Zero Trust Architecture [0.0]
  Zero Trust Architecture (ZTA) represents a transformative approach to modern cybersecurity. ZTA shifts the security paradigm by assuming that no user, device, or system can be trusted by default. This paper explores the key components of ZTA, such as identity and access management (IAM), micro-segmentation, continuous monitoring, and behavioral analytics.
  arXiv  Detail & Related papers  (2024-10-23T21:53:16Z)
• SafeEmbodAI: a Safety Framework for Mobile Robots in Embodied AI Systems [5.055705635181593]
  Embodied AI systems, including AI-powered robots that autonomously interact with the physical world, stand to be significantly advanced. Improper safety management can lead to failures in complex environments and make the system vulnerable to malicious command injections. We propose textitSafeEmbodAI, a safety framework for integrating mobile robots into embodied AI systems.
  arXiv  Detail & Related papers  (2024-09-03T05:56:50Z)
• AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways [10.16690494897609]
  An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs. This survey delves into the emerging security threats faced by AI agents, categorizing them into four critical knowledge gaps. By systematically reviewing these threats, this paper highlights both the progress made and the existing limitations in safeguarding AI agents.
  arXiv  Detail & Related papers  (2024-06-04T01:22:31Z)
• Generative AI in Cybersecurity [0.0]
  Generative Artificial Intelligence (GAI) has been pivotal in reshaping the field of data analysis, pattern recognition, and decision-making processes. As GAI rapidly progresses, it outstrips the current pace of cybersecurity protocols and regulatory frameworks. The study highlights the critical need for organizations to proactively identify and develop more complex defensive strategies to counter the sophisticated employment of GAI in malware creation.
  arXiv  Detail & Related papers  (2024-05-02T19:03:11Z)
• Large language models in 6G security: challenges and opportunities [5.073128025996496]
  We focus on the security aspects of Large Language Models (LLMs) from the viewpoint of potential adversaries. This will include the development of a comprehensive threat taxonomy, categorizing various adversary behaviors. Also, our research will concentrate on how LLMs can be integrated into cybersecurity efforts by defense teams, also known as blue teams.
  arXiv  Detail & Related papers  (2024-03-18T20:39:34Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• 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)
• Constrained Reinforcement Learning for Robotics via Scenario-Based Programming [64.07167316957533]
  It is crucial to optimize the performance of DRL-based agents while providing guarantees about their behavior. This paper presents a novel technique for incorporating domain-expert knowledge into a constrained DRL training loop. Our experiments demonstrate that using our approach to leverage expert knowledge dramatically improves the safety and the performance of the agent.
  arXiv  Detail & Related papers  (2022-06-20T07:19:38Z)
• 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)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• 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.