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An Efficient Hybrid Approach for Intrusion Detection in Cyber Traffic Using Autoencoders

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Abstract

Intrusion detection is an essential security issue in the present digital climate. Spiteful cyber attacks can frequently hide/sneak in abundant volumes of regular data in lopsided network traffic. In cyberspace, it has a great level of stealth and opacity, making it crucial for network-based intrusion detection systems (NIDS) to assure the accuracy in detection and the given timelines. The false-positive issue is one of the inherent drawbacks of NIDS, which are widely employed to identify threats and safeguard networks. Imbalance classes and unreasonable network datasets are the main reasons behind these false positives. This paper proposes an autoencoder-based anomaly detection methodology, which uses a reconstruction error approach to generate attack samples, which are less in number in the training dataset. Feature rngineering is also performed in this paper using the recursive feature elimination method. We utilized the NSL-KDD dataset for this experiment. The result shows that our approach is better than various modern approaches in terms of various metrics such as accuracy, precision, recall, and F1 score.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur, India

    Kanak Giri, Mukesh Gupta & Pankaj Dadheech

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  1. Kanak Giri
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  2. Mukesh Gupta
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Correspondence to Kanak Giri.

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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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Giri, K., Gupta, M. & Dadheech, P. An Efficient Hybrid Approach for Intrusion Detection in Cyber Traffic Using Autoencoders. SN COMPUT. SCI. 4, 498 (2023). https://doi.org/10.1007/s42979-023-01865-3

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