Genetic Algorithm-based Optimal Deep Neural Network for Detecting Network Intrusions

Sourav Adhikary; Md. Musfique Anwar; Mohammad Jabed Morshed Chowdhury; Iqbal H. Sarker

doi:10.20944/preprints202201.0465.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 27 January 2022 / Approved: 31 January 2022 / Online: 31 January 2022 (13:26:18 CET)

Computer network attacks are evolving in parallel with the evolution of hardware and neural network architecture. Despite major advancements in Network Intrusion Detection System (NIDS) technology, most implementations still depend on signature-based intrusion detection systems, which can’t identify unknown attacks. Deep learning can help NIDS to detect novel threats since it has a strong generalization ability. The deep neural network’s architecture has a significant impact on the model’s results. We propose a genetic algorithm based model to find the optimal number of hidden layers and the number of neurons in each layer of the deep neural network (DNN) architecture for the network intrusion detection binary classification problem. Experimental results demonstrate that the proposed DNN architecture shows better performance than classical machine learning algorithms at a lower computational cost.

genetic algorithm; deep neural network; hidden layer; optimal architecture; intrusion detection

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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Genetic Algorithm-based Optimal Deep Neural Network for Detecting Network Intrusions

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