Intrusion Outlier Neutralizer: A Novel LOF-Based Framework for IoT Malware Detection

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Author(s): Angel Justo Jones (Capitol Technology University, USA & University of Virginia, USA)
Copyright: 2024
Pages: 15
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.ch014

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

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Abstract

The proliferation of the internet of things (IoT) has significantly enhanced the convenience and functionality of various applications ranging from personal devices to industrial systems. However, this expansion has also escalated the vulnerability of these networks to sophisticated malware attacks, posing a critical threat to the security and reliability of IoT systems. This chapter introduces an innovative defense framework based on the local outlier factor (LOF) technique for effective malware detection in IoT networks. The framework employs a systematic approach, starting from data collection and preprocessing to the application of LOF for anomaly detection. The research demonstrates through empirical analysis that the LOF-based method outperforms traditional malware detection techniques, offering higher precision, recall, and lower false positive rates. The comparative analysis with existing IoT malware detection methods such as Mobile-net IoT and Image-net IoT further validates the superiority of the LOF approach.

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Intrusion Outlier Neutralizer: A Novel LOF-Based Framework for IoT Malware Detection

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