Enhancing System Security by Intrusion Detection Using Deep Learning

• Published in: Databases Theory and Applications Vol. 13459; pp. 169 - 176
• Main Authors: Sama, Lakshit, Wang, Hua, Watters, Paul
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 9783031155116; 3031155114
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-15512-3_14

Network intrusion detection has become a hot topic in cyber security research due to better advancements in deep learning. The research is lacking an objective comparison of the various deep learning models in a controlled setting, notably on recent intrusion detection datasets, despite the fact that several outstanding studies address the growing body of research on the subject. In this paper, a network intrusion scheme is developed as a solution of the discussed issue. The four different models are build and are experimented with NSL-KDD dataset. These deep learning models are LightGBM, XGBoost, LSTM, and decision tree. For the validation of the proposed scheme, the proposed scheme is also experimented with UNSW-NB15 dataset and CIC-IDS2017. However, the experiments concluded that the proposed scheme outperforms and the discussion is also illustrated.