Start / Redefining CyberSecurity / Deep backdoors in deep reinforcement learning agents a black hat usa 2024 conversation with vas mavroudis and jamie gawith on location coverage with sean martin and marco ciappelli

Deep Backdoors in Deep Reinforcement Learning Agents | A Black Hat USA 2024 Conversation with Vas Mavroudis and Jamie Gawith | On Location Coverage with Sean Martin and Marco Ciappelli

24 min • 1 augusti 2024

Guests:

Vas Mavroudis, Principal Research Scientist, The Alan Turing Institute

Website | https://mavroud.is/

At BlackHat | https://www.blackhat.com/us-24/briefings/schedule/speakers.html#vasilios-mavroudis-34757

Jamie Gawith, Assistant Professor of Electrical Engineering, University of Bath

On LinkedIn | https://www.linkedin.com/in/jamie-gawith-63560b60/

At BlackHat | https://www.blackhat.com/us-24/briefings/schedule/speakers.html#jamie-gawith-48261

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Hosts:

Sean Martin, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining CyberSecurity Podcast [@RedefiningCyber]

On ITSPmagazine | https://www.itspmagazine.com/sean-martin

Marco Ciappelli, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining Society Podcast

On ITSPmagazine | https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/marco-ciappelli

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Episode Notes

As Black Hat Conference 2024 approaches, Sean Martin and Marco Ciappelli are gearing up for a conversation about the complexities of deep reinforcement learning and the potential cybersecurity threats posed by backdoors in these systems. They will be joined by Vas Mavroudis from the Alan Turing Institute and Jamie Gawith from the University of Bath, who will be presenting their cutting-edge research at the event.

Setting the Stage: The discussion begins with Sean and Marco sharing their excitement about the upcoming conference. They set a professional and engaging tone, seamlessly leading into the introduction of their guests, Jamie and Vas.

The Core Discussion: Sean introduces the main focus of their upcoming session, titled "Backdoors in Deep Reinforcement Learning Agents." Expressing curiosity and anticipation, he invites Jamie and Vas to share more about their backgrounds and the significance of their work in this area.

Expert Introductions: Jamie Gawith explains his journey from working in power electronics and nuclear fusion to focusing on cybersecurity. His collaboration with Vas arose from a shared interest in using reinforcement learning agents for controlling nuclear fusion reactors. He describes the crucial role these agents play and the potential risks associated with their deployment in critical environments.

Vas Mavroudis introduces himself as a principal research scientist at the Alan Turing Institute, leading a team focused on autonomous cyber defense. His work involves developing and securing autonomous agents tasked with defending networks and systems from cyber threats. The conversation highlights the vulnerabilities of these agents to backdoors and the need for robust security measures.

Deep Dive into Reinforcement Learning: Vas offers an overview of reinforcement learning, highlighting its differences from supervised and unsupervised learning. He emphasizes the importance of real-world experiences in training these agents to make optimal decisions through trial and error. The conversation also touches on the use of deep neural networks, which enhance the capabilities of reinforcement learning models but also introduce complexities that can be exploited.

Security Concerns: The discussion then shifts to the security challenges associated with reinforcement learning models. Vas explains the concept of backdoors in machine learning and the unique challenges they present. Unlike traditional software backdoors, these are hidden within the neural network layers, making detection difficult.

Real-World Implications: Jamie discusses the practical implications of these security issues, particularly in high-stakes scenarios like nuclear fusion reactors. He outlines the potential catastrophic consequences of a backdoor-triggered failure, underscoring the importance of securing these models to prevent malicious exploitation.

Looking Ahead: Sean and Marco express their anticipation for the upcoming session, highlighting the collaborative efforts of Vas, Jamie, and their teams in tackling these critical issues. They emphasize the significance of this research and its implications for the future of autonomous systems.

Conclusion: This pre-event conversation sets the stage for a compelling session at Black Hat Conference 2024. It offers attendees a preview of the insights and discussions they can expect about the intersection of deep reinforcement learning and cybersecurity. The session promises to provide valuable knowledge on protecting advanced technologies from emerging threats.

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