Publication

Publication Type : Conference Paper

Publisher : IEEE

Source : 2023 International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS)

Url : https://doi.org/10.1109/iq-cchess56596.2023.10391651

Campus : Amritapuri

School : School of Computing

Year : 2023

This study discusses a Network Intrusion Detection System  leveraging Deep Belief Networks to classify network traffic into multiple categories. By stacking Multiple Restricted Boltzmann Machines, the NIDS model fosters a Deep Belief Network-a generative graphical model. This implied approach is particularly good at identifying and identifying high-dimensional representations. Using Gibbs Sampling and the Contrastive Divergence method, the Deep Belief Network is first pre-trained unsupervised before being fine-tuned supervised. Our research study utilized the CICIDS2018 dataset to train and assess the effectiveness of our proposed DBN technique. Our empirical findings show that our two-phase training technique not only maintains impressive performance against other types of assaults, but also dramatically improves detection accuracy when comprehending samples of peculiar attacks.

Cite this Research Publication : A Akshai, M Anushri, P Sonu, A New Systematic Network Intrusion Detection System Using Deep Belief Network, 2023 International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), IEEE, 2023, https://doi.org/10.1109/iq-cchess56596.2023.10391651