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Güneş Kayacık, H., Nur Zincir-Heywood, A. and Heywood, M.I. (2005) Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets. Proceedings of the 3rd Annual Conference on Privacy, Security and Trust, Vol. 94, 85-89.

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• TITLE: Task-Specific Feature Selection and Detection Algorithms for IoT-Based Networks

  AUTHORS: Yang Gyun Kim, Benito Mendoza, Ohbong Kwon, John Yoon

  KEYWORDS: Cybersecurity, Features Selection, Information Gain, Particle Swarm Optimization, Intrusion Detection System, Machine Learning, Decision Tree, Network Attacks, IoT Network

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.10, October 27, 2022

  ABSTRACT: As IoT devices become more ubiquitous, the security of IoT-based networks becomes paramount. Machine Learning-based cybersecurity enables autonomous threat detection and prevention. However, one of the challenges of applying Machine Learning-based cybersecurity in IoT devices is feature selection as most IoT devices are resource-constrained. This paper studies two feature selection algorithms: Information Gain and PSO-based, to select a minimum number of attack features, and Decision Tree and SVM are utilized for performance comparison. The consistent use of the same metrics in feature selection and detection algorithms substantially enhances the classification accuracy compared to the non-consistent use in feature selection by Information Gain (entropy) and Tree detection algorithm by classification. Furthermore, the Tree with consistent feature selection is comparable to the ensemble that provides excellent performance at the cost of computation complexity.