Autonomous cyber defence by quantum-inspired deep reinforcement learning

Feng, Wenbo, Vyas, Sanyam and Li, Tingting ORCID: https://orcid.org/0000-0002-9448-1655 2025. Autonomous cyber defence by quantum-inspired deep reinforcement learning. Presented at: 11th International Conference on Information Systems Security and Privacy, Porto, Portugal, 20-22 February 2025. Published in: Di Pietro, Roberto, Renaud, Karen and Mori, Paolo eds. Proceedings of the 11th International Conference on Information Systems Security and Privacy. , vol.2 SciTePress, pp. 184-191. 10.5220/0013151800003899

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Abstract

With the rapid advancement of computing technologies, the frequency and complexity of cyber-attacks have escalated. Autonomous Cyber Defence (ACD) has emerged to combat these threats, aiming to train defensive agents that can autonomously respond to cyber incidents at machine speed and scale, similar to human defenders. One of the main challenges in ACD is enhancing the training efficiency of defensive agents in complex network environments, typically using Deep Reinforcement Learning (DRL). This work addresses this challenge by employing quantum-inspired methods. When coupled with Quantum-Inspired Experience Replay (QER) buffers and the Quantum Approximate Optimization Algorithm (QAOA), we demonstrate an improvement in training the defence agents against attacking agents in real-world scenarios. While QER and QAOA show great potential for enhancing agent performance, they introduce substantial computational demands and complexity, particularly during the training phase.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	SciTePress
ISBN:	978-989-758-735-1
ISSN:	2184-4356
Date of First Compliant Deposit:	11 March 2025
Date of Acceptance:	22 February 2025
Last Modified:	11 Mar 2025 13:21
URI:	https://orca.cardiff.ac.uk/id/eprint/176590

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