Feature optimization using a two-tier hybrid optimizer in an Internet of Things network

Agrawal, Akhileshwar Prasad  ; Singh, Nanhay

International Journal of Applied Mathematics and Computer Science, Tome 33 (2023) no. 2, pp. 313-326.

Résumé

The growing use of the Internet of Things (IoT) in smart applications necessitates improved security monitoring of IoT components. The security of such components is monitored using intrusion detection systems which run machine learning (ML) algorithms to classify access attempts as anomalous or normal. However, in this case, one of the issues is the large length of the data feature vector that any ML or deep learning technique implemented on resource-constrained intelligent nodes must handle. In this paper, the problem of selecting an optimal-feature set is investigated to reduce the curse of data dimensionality. A two-layered approach is proposed: the first tier makes use of a random forest while the second tier uses a hybrid of gray wolf optimizer (GWO) and the particle swarm optimizer (PSO) with the k-nearest neighbor as the wrapper method. Further, differential weight distribution is made to the local-best and global-best positions in the velocity equation of PSO. A new metric, i.e., the reduced feature to accuracy ratio (RFAR), is introduced for comparing various works. Three data sets, namely, NSLKDD, DS2OS and BoTIoT, are used to evaluate and validate the proposed work. Experiments demonstrate improvements in accuracy up to 99.44

Détail

Keywords: IoT, Internet of Things, anomaly mitigation, GWO, Gray Wolf Optimizer, feature optimization, PSO, particle swarm optimizer
Mots-clés : IoT, Internet Rzeczy, GWO, optymalizacja funkcji, PSO

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