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Lookup NU author(s): Yizhou Shen, Dr Carlton Shepherd, Dr Mujeeb AhmedORCiD
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© IEEE. Internet of Things (IoT) systems are susceptible to compromise due to malware propagation, leading to the data breach and information theft. In this paper, we propose a proactive deception-oriented hypergame-theoretic malware propagation-mitigation (DHMPM) model between IoT nodes and edge devices under asymmetric information in edge intelligence (EI)-enabled IoT systems. We then explore malware-propagated deceptive defense strategies based on deep reinforcement learning. Specifically, IoT nodes and edge devices continually adjust their strategies based on obtained utilities under beliefs perceived by uncertainties from the game environment and system dynamics. Built upon the proposed game DHMPM, we next apply spiking neural networks (SNNs) into deep Q-network to form hypergame-theoretic deep spiking Q-network (HGDSQN), practically converging to the optimal malware-propagated deceptive defense strategy in EI-enabled IoT systems. Such SNNs can simulate biological brains with the pulse communication mechanism and break through the bottleneck of temporal processing in traditional models with deep neural networks, realizing intelligent decision-making and real-time malware defense. We eventually perform experimental simulations that assess the effect of attack arrival probability and learning rate on the optimal learning strategy selection, demonstrating the effectiveness of the proposed HGDSQN algorithm.
Author(s): Shen Y, Shepherd C, Ahmed CM, Shen S, Yu S
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Services Computing
Year: 2025
Volume: 18
Issue: 3
Pages: 1487-1499
Print publication date: 01/05/2025
Online publication date: 18/04/2025
Acceptance date: 08/04/2025
ISSN (electronic): 1939-1374
Publisher: IEEE
URL: https://doi.org/10.1109/TSC.2025.3562355
DOI: 10.1109/TSC.2025.3562355
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