Deep Learning Approaches for Intrusion Detection
Azar Abid Salih, Siddeeq Y. Ameen, Subhi R. M. Zeebaree, Mohammed A. M. Sadeeq, Shakir Fattah Kak, Naaman Omar, Ibrahim Mahmood Ibrahim, Hajar Maseeh Yasin, Zryan Najat Rashid, Zainab Salih Ageed
Asian Journal of Research in Computer Science · pp. 50–64 · Published 17 Jun 2021
10.9734/ajrcos/2021/v9i430229Abstract
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.
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