Browse by author
Lookup NU author(s): Dr Mujeeb AhmedORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Malware poses a significant threat to Internet of Things (IoT) systems, leading to unauthorized access and data breaches. Evolving malware continues to pose severe threats to IoT systems, necessitating defense mechanisms capable of adaptive learning and real-time strategic decision-making. To this end, we present a novel IoT malware propagation and patching model based on differential games and spiking neural networks in edge intelligence (EI)-enabled IoT systems. Specifically, built upon optimal control theory, we reveal the process of dynamic evolution between infected IoT devices and associated edge nodes based on differential games under malware propagation in EI-enabled IoT systems. The dynamic state transitions are described by differential equations, followed by introducing attack/defense intention for theoretically attaining the optimal IoT malware patching strategies. Moreover, we design a novel IoT malware propagation-patching approach named differential games-based deep spiking Q-network (DGDSQ) for practical patch optimization decisions in EI-enabled IoT systems. Additionally, we conduct experimental simulations to comprehend that DGDSQ-assisted edge nodes are more effective than the double deep Q-network and the dueling double deep Q-network against IoT malware propagation.
Author(s): Yizhou S, Carlton S, Ahmed CM, Shigen S
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Dependable and Secure Computing
Year: 2026
Pages: epub ahead of print
Online publication date: 29/05/2026
Acceptance date: 22/05/2026
Date deposited: 06/08/2026
ISSN (print): 1545-5971
ISSN (electronic): 1941-0018
Publisher: IEEE
URL: https://doi.org/10.1109/TDSC.2026.3698145
DOI: 10.1109/TDSC.2026.3698145
ePrints DOI: 10.57711/f6jm-3j89
Altmetrics provided by Altmetric
