Enhancing Intrusion Detection Systems Using a Deep Learning and Data Augmentation Approach

Mohammad, Rasheed and Saeed, Faisal and Almazroi, Abdulwahab Ali and Alsubaei, Faisal S. and Almazroi, Abdulaleem Ali (2024) Enhancing Intrusion Detection Systems Using a Deep Learning and Data Augmentation Approach. Systems, 12 (3). p. 79. ISSN 2079-8954

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Cybersecurity relies heavily on the effectiveness of intrusion detection systems (IDSs) in securing business communication because they play a pivotal role as the first line of defense against malicious activities. Despite the wide application of machine learning methods for intrusion detection, they have certain limitations that might be effectively addressed by leveraging different deep learning architectures. Furthermore, the evaluation of the proposed models is often hindered by imbalanced datasets, limiting a comprehensive assessment of model efficacy. Hence, this study aims to address these challenges by employing data augmentation methods on four prominent datasets, the UNSW-NB15, 5G-NIDD, FLNET2023, and CIC-IDS-2017, to enhance the performance of several deep learning architectures for intrusion detection systems. The experimental results underscored the capability of a simple CNN-based architecture to achieve highly accurate network attack detection, while more complex architectures showed only marginal improvements in performance. The findings highlight how the proposed methods of deep learning-based intrusion detection can be seamlessly integrated into cybersecurity frameworks, enhancing the ability to detect and mitigate sophisticated network attacks. The outcomes of this study have shown that the intrusion detection models have achieved high accuracy (up to 91% for the augmented CIC-IDS-2017 dataset) and are strongly influenced by the quality and quantity of the dataset used.

Item Type: Article
Identification Number: https://doi.org/10.3390/systems12030079
15 February 2024Accepted
1 March 2024Published Online
Uncontrolled Keywords: data augmentation, deep learning, cyber-attack, intrusion detection, machine learning
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 11 Mar 2024 15:17
Last Modified: 11 Mar 2024 15:17
URI: https://www.open-access.bcu.ac.uk/id/eprint/15327

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