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Enhancing Intrusion Detection System Performance to Detect Attacks on Edge of Things

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Abstract

Edge computing is an emerging network system under which the data source and data process are brought to the end node (edge device) for computation and which is used to speed up response time, save bandwidth, and increase the efficiency of the application. Edge of Things (EoT) is the group of entirely Edge node that has the power to connect to the network/internet to gather and share data in a computing environment. Protecting EoT environments with traditional Intrusion Detection Systems (IDSs) is a severe problem because of the large number of EoT devices and various types of EoT devices being used due to large amounts of data being collected and data processed on the network. Edge architecture consists of several layers. Due to architectural changes, privacy and security concerns in the EoT are moving to different layers of edge architecture. As a result, it can be difficult to detect intrusion threats in decentralized systems. Therefore, intrusion detection systems are needed. Several approaches to IDS have been proposed and developed to reduce and avoid cyber-attacks, but new techniques still need to be enhanced. This study aims to offer an improved IDS model for classifying attacks on EoT networks. To protect EoT network, an improved EoT-IDS is proposed by applying multiple machine learning (ML) models. This research work uses the combination-based grouping method for optimal class feature selection. Afterward, the filter-based feature selection technique was performed for optimal reduced features. We focus on the optimal features selection framework, because for better accuracy and anomaly detection of ML models, the effectiveness of feature selection is imperative. The research was executed on the UNSW-NB15 dataset. The performance results show an accuracy of 98.72%, a detection rate (DR) of 98.47%, and a false alarm rate (FAR) of 0.87% for optimal reduced features using the Random Forest classifier.

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  1. Faculty of Science, Department of Computer Science, Gurukula Kangri (Deemed to be University), Haridwar, 249404, India

    Vipin Kumar, Vivek Kumar, Nipur Singh & Raj Kumar

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  1. Vipin Kumar
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Correspondence to Vipin Kumar.

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This article is part of the topical collection “Industrial IoT and Cyber-Physical Systems” guest edited by Arun K Somani, Seeram Ramakrishnan, Anil Chaudhary and Mehul Mahrishi.

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Kumar, V., Kumar, V., Singh, N. et al. Enhancing Intrusion Detection System Performance to Detect Attacks on Edge of Things. SN COMPUT. SCI. 4, 802 (2023). https://doi.org/10.1007/s42979-023-02242-w

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