Salih, Azar Abid and Ameen, Siddeeq Y. and Zeebaree, Subhi R. M. and Sadeeq, Mohammed A. M. and Kak, Shakir Fattah and Omar, Naaman and Ibrahim, Ibrahim Mahmood and Yasin, Hajar Maseeh and Rashid, Zryan Najat and Ageed, Zainab Salih (2021) Deep Learning Approaches for Intrusion Detection. Asian Journal of Research in Computer Science, 9 (4). pp. 50-64. ISSN 2581-8260
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Abstract
Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.
Item Type: | Article |
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Subjects: | STM Archives > Computer Science |
Depositing User: | Unnamed user with email support@stmarchives.com |
Date Deposited: | 17 Mar 2023 07:36 |
Last Modified: | 31 Jul 2024 13:14 |
URI: | http://science.scholarsacademic.com/id/eprint/132 |