Open AccessDeep Learning Approaches for Intrusion Detection
Author(s) -
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
Publication year - 2021
Publication title -
asian journal of research in computer science
Language(s) - English
Resource type - Journals
ISSN - 2581-8260
DOI - 10.9734/ajrcos/2021/v9i430229
Subject(s) - intrusion detection system , computer science , deep learning , artificial intelligence , network security , machine learning , intrusion prevention system , feature (linguistics) , intrusion , anomaly based intrusion detection system , data mining , computer security , linguistics , philosophy , geochemistry , geology
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.