Autonomous Cyber Defence by Quantum-Inspired Deep Reinforcement Learning

Wenbo Feng, Sanyam Vyas, Tingting Li

2025

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. To address this, we also explore a more practical and efficient approach by using QAOA with Prioritised Experience Replay (PER), achieving a balance between computational feasibility and performance.

Paper Citation

in Harvard Style

Feng W., Vyas S. and Li T. (2025). Autonomous Cyber Defence by Quantum-Inspired Deep Reinforcement Learning. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 184-191. DOI: 10.5220/0013151800003899

in Bibtex Style

@conference{icissp25,
author={Wenbo Feng and Sanyam Vyas and Tingting Li},
title={Autonomous Cyber Defence by Quantum-Inspired Deep Reinforcement Learning},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP},
year={2025},
pages={184-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013151800003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP
TI - Autonomous Cyber Defence by Quantum-Inspired Deep Reinforcement Learning
SN - 978-989-758-735-1
AU - Feng W.
AU - Vyas S.
AU - Li T.
PY - 2025
SP - 184
EP - 191
DO - 10.5220/0013151800003899
PB - SciTePress