Open Access

ARTICLE

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Ashraf S. Mashaleh^1,2,*, Noor Farizah Binti Ibrahim¹, Mohammad Alauthman³, Mohammad Almseidin⁴, Amjad Gawanmeh⁵

1 School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia
2 Department Computer Center, Al-Balqa Applied University, Salt, Jordan
3 Department of Information Security, Faculty of Information Technology, University of Petra, Amman, Jordan
4 Department of Computer Science, Tafila Technical University, Tafila, Jordan
5 College of Engineering and IT, University of Dubai, Dubai, United Arab Emirates

* Corresponding Author: Ashraf S. Mashaleh. Email:

Computers, Materials & Continua 2024, 78(2), 2245-2267. https://doi.org/10.32604/cmc.2023.047323

Received 02 November 2023; Accepted 20 December 2023; Issue published 27 February 2024

Abstract

Increasing Internet of Things (IoT) device connectivity makes botnet attacks more dangerous, carrying catastrophic hazards. As IoT botnets evolve, their dynamic and multifaceted nature hampers conventional detection methods. This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization (PSO) to address the risks associated with IoT botnets. Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically. Fuzzy component settings are optimized using PSO to improve accuracy. The methodology allows for more complex thinking by transitioning from binary to continuous assessment. Instead of expert inputs, PSO data-driven tunes rules and membership functions. This study presents a complete IoT botnet risk assessment system. The methodology helps security teams allocate resources by categorizing threats as high, medium, or low severity. This study shows how CICIoT2023 can assess cyber risks. Our research has implications beyond detection, as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

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Keywords

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