An improved long short term memory network for intrusion detection

Awad, Asmaa Ahmed; Ali, Ahmed Fouad; Gaber, Tarek

Abstract

Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. The performance of ILSTM and the intrusion detection system were evaluated using two public datasets (NSL-KDD dataset and LITNET-2020) under nine performance metrics. The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. The ILSTM achieved an accuracy of 93.09% and a precision of 96.86% while LSTM gave an accuracy of 82.74% and a precision of 76.49%. Also, the ILSTM performed better than LSTM in both datasets. In addition, the statistical analysis showed that ILSTM is more statistically significant than LSTM. Further, the proposed ISTLM gave better results of multiclassification of intrusion types such as DoS, Prob, and U2R attacks.

Citation

Awad, A. A., Ali, A. F., & Gaber, T. (in press). An improved long short term memory network for intrusion detection. PloS one, 18(8), e0284795. https://doi.org/10.1371/journal.pone.0284795

• Journal Article Type: Article
• Acceptance Date: Apr 7, 2023
• Online Publication Date: Aug 1, 2023
• Deposit Date: Aug 15, 2023
• Publicly Available Date: Aug 15, 2023
• Journal: PLOS ONE
• Print ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 8
• Pages: e0284795
• DOI: https://doi.org/10.1371/journal.pone.0284795