ArticleSensors (Basel, Switzerland)2025
A Hybrid Approach for IoT Security: Combining Ensemble Learning with Fuzzy Logic.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Explainable AI-Based Intrusion Detection Systems for IoT Environments: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2026Review
- Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger.Sensors (Basel, Switzerland) · 2026Article
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1 author.
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Abstract
The rapid expansion of Internet of Things (IoT) devices has led to substantial progress in various fields. The diverse and resource-limited characteristics of IoT devices make them susceptible to numerous cyber threats, especially malware. Traditional security approaches fall short of effectively addressing these challenges. In this paper, a novel hybrid approach based on the integration of ensemble learning and fuzzy logic is proposed to enhance IoT security. While the ensemble learning model combines multiple classifiers to improve detection accuracy, fuzzy logic enables a more flexible and interpretable assessment of the security status of IoT systems. Experimental results reveal that the proposed framework provides high-accuracy malware detection and, through the fuzzy system built upon the rule base derived from the ensemble model, offers a more flexible and human intuition-oriented evaluation capability. This study offers an effective solution for ensuring IoT system security, providing an applicable approach across diverse IoT ecosystems.
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