Using Deep Learning Techniques for Network Intrusion Detection

Sara Al-Emadi, Aisha Al-Mohannadi, Felwa Al-Senaid

20202020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)72 citationsDOI

Abstract

In recent years, there has been a significant increase in network intrusion attacks which raises a great concern from the privacy and security aspects. Due to the advancement of the technology, cyber-security attacks are becoming very complex such that the current detection systems are not sufficient enough to address this issue. Therefore, an implementation of an intelligent and effective network intrusion detection system would be crucial to solve this problem. In this paper, we use deep learning techniques, namely, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to design an intelligent detection system which is able to detect different network intrusions. Additionally, we evaluate the performance of the proposed solution using different evaluation matrices and we present a comparison between the results of our proposed solution to find the best model for the network intrusion detection system.

Topics & Concepts

Intrusion detection systemComputer scienceDeep learningConvolutional neural networkArtificial intelligenceRecurrent neural networkNetwork securityMachine learningArtificial neural networkIntrusion prevention systemComputer securityNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques