Deep Learning Approaches for Intrusion Detection
Azar Abid Salih *
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Siddeeq Y. Ameen
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Subhi R. M. Zeebaree
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Mohammed A. M. Sadeeq
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Shakir Fattah Kak
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Naaman Omar
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Ibrahim Mahmood Ibrahim
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Hajar Maseeh Yasin
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Zryan Najat Rashid
Sulaimani Polytechnic University, Sulaimani, Kurdistan Region, Iraq.
Zainab Salih Ageed
Nawroz University, Duhok, Kurdistan Region, Iraq.
*Author to whom correspondence should be addressed.
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.
Keywords: Deep learning, intrusion detection, network attacks, intrusion datasets, security service