Electrical & Computer Engineering: Faculty Publications

A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15

Document Type

Conference Proceeding

Publication Date

5-23-2023

Publication Title

2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023 Proceedings

DOI

10.1109/SERA57763.2023.10197694

ISBN

9798350345889

Abstract

Intrusion Detection System (IDS) is a crucial security mechanism for protecting computer networks from cyber-Attacks. Deep learning models have the potential to detect attack types by leveraging their ability to learn and extract features from large volumes of data. In this study, we compare the performance of four different deep learning algorithms for IDS: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional LSTM, and bidirectional GRU. We evaluate the attack prediction accuracy for three types of attacks: Denial of Service (DoS), Generic, and Exploits. We vary each algorithm's range parameter and epochs and determine the best parameter combination sets for achieving the highest accuracy. Our experimental results demonstrate that increased range parameters influence the accuracy of LSTM, bi-LSTM, and Bi-GRU models. Ultimately, GRU proved to have the most outstanding performance among the four algorithms tested.

Comments

Georgia Southern University faculty members, Lei Chen, Jongyeop Kim, Yiming Ji, and Rami Haddad co-authored "A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15."

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