A deep learning approach for network intrusion detection using a small features vector

Ghani, Humera, Virdee, Bal Singh and Salekzamankhani, Shahram (2023) A deep learning approach for network intrusion detection using a small features vector. Journal of Cybersecurity and Privacy, 3 (3). pp. 451-463. ISSN 2624-800X

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Official URL: https://www.mdpi.com/2624-800X/3/3/23

Abstract / Description

With the growth in network usage, there has been a corresponding growth in the nefarious exploitation of this technology. A wide array of techniques is now available that can be used to deal with cyberattacks, and one of them is network intrusion detection. Artificial Intelligence (AI) and Machine Learning (ML) techniques have extensively been employed to identify network anomalies. This paper provides an effective technique to evaluate the classification performance of a deep learning-based Feedforward Neural Network (FFNN) classifier. A small feature vector is used to detect network traffic anomalies in the UNSW-NB15 and NSL-KDD datasets. The results show that a large feature set can have redundant and unuseful features, and it requires high computation power. The proposed technique exploits a small feature vector and achieves better classification accuracy.

Item Type: Article
Uncontrolled Keywords: deep learning; feedforward neural network; network intrusion detection; UNSW-NB15; NSL-KDD
Subjects: 000 Computer science, information & general works
600 Technology
600 Technology > 620 Engineering & allied operations
Department: School of Computing and Digital Media
Depositing User: Bal Virdee
Date Deposited: 03 Aug 2023 11:46
Last Modified: 03 Aug 2023 11:46
URI: https://repository.londonmet.ac.uk/id/eprint/8667


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