Advancing IoT Security Posture K-Means Clustering for Malware Detection

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Author(s): Ali Dayoub (Capitol Technology University, USA)and Marwan Omar (Capitol Technology University, USA & Illinois Institute of Technology, USA)
Copyright: 2024
Pages: 19
Source title: Innovations, Securities, and Case Studies Across Healthcare, Business, and Technology
Source Author(s)/Editor(s): Darrell Norman Burrell (Marymount University, USA)
DOI: 10.4018/979-8-3693-1906-2.ch012

Keywords: Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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Abstract

The ever-expanding internet of things (IoT) ecosystem has brought with it new challenges in terms of security and malware detection. In this chapter, the authors introduce a novel approach to IoT malware detection using K-means clustering. They present comprehensive results and analysis demonstrating the effectiveness of the approach compared to traditional mobile-net IoT and image-net IoT methods. The approach achieves superior precision, recall, and overall performance, while maintaining a low false positive rate. This research provides valuable insights into the potential of K-means clustering in IoT security and sets the stage for further research in this critical domain.

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Advancing IoT Security Posture K-Means Clustering for Malware Detection

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