Open AccessDesign of a Network Intrusion Detection System Using Complex Deep Neuronal Networks
Author(s) -
Mohammed Al-Shabi
Publication year - 2021
Publication title -
international journal of communication networks and information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 20
eISSN - 2076-0930
pISSN - 2073-607X
DOI - 10.54039/ijcnis.v13i3.5148
Subject(s) - intrusion detection system , computer science , artificial intelligence , deep learning , machine learning , anomaly based intrusion detection system , data mining
Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy.