Design of a Network Intrusion Detection System Using Complex Deep Neuronal Networks

Mohammed Abdulhammed Al-Shabi(1*)
(1) Department of Management Information System, College of Business Administration, Taibah University, Saudi Arabia
(*) Corresponding Author
DOI : 10.54039/ijcnis.v13i3.5148


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.


Cyber security, Intrusion detection system, Complex deep neural networks, Deep learning, KDD CUP99 dataset, Computer network.


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International Journal of Communication Networks and Information Security (IJCNIS)               ISSN: 2073-607X (Online)