- Title
- An intelligent and efficient network intrusion detection system using deep learning
- Creator
- Qazi, Emad-ul-Haq; Imran, Muhammad; Haider, Noman; Shoaib, Muhammad; Razzak, Imran
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/188131
- Identifier
- vital:17240
- Identifier
-
https://doi.org/10.1016/j.compeleceng.2022.107764
- Identifier
- ISSN:0045-7906 (ISSN)
- Abstract
- With continuously escalating threats and attacks, accurate and timely intrusion detection in communication networks is challenging. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges due to the continuous increase of new threats that current systems do not understand. Motivated by the outstanding performance of deep learning (DL) in many detection and recognition tasks, we introduce an intelligent and efficient network intrusion detection system (NIDS) based on DL. This study proposes a non-symmetric deep auto-encoder for network intrusion detection problems and presents its detailed functionality and performance. We validate the robustness and effectiveness of the proposed NIDS using a benchmark dataset, i.e., KDD CUP'99. Our DL-based method is implemented in the TensorFlow library and GPU framework, and it achieves an accuracy of 99.65%. The proposed system can be used in network security research domains and DL-based detection and classification systems. © 2022
- Publisher
- Elsevier Ltd
- Relation
- Computers and Electrical Engineering Vol. 99, no. (2022), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 Elsevier Ltd. Al
- Subject
- 4602 Artificial intelligence; 4606 Distributed computing and systems software; 4008 Electrical engineeringAuto-encoder; Deep learning; Intrusion detection; Network security; SVM
- Reviewed
- Funder
- Deanship of Scientific Research, King Saud University, RG-1439–036 Funding text 1: This work is supported by the Deanship of Scientific Research at King Saud University through the research group project number RG-1439–036.
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