An intrusion detection system based on GWO-CNN-BiLSTM

Fan Yu; Bingxin Tian; Hengquan Yu; Jiuchun Ren

doi:10.1117/12.3038081

7 August 2024 An intrusion detection system based on GWO-CNN-BiLSTM

Fan Yu, Bingxin Tian, Hengquan Yu, Jiuchun Ren

Author Affiliations +

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132292C (2024) https://doi.org/10.1117/12.3038081
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

Network security is a critical concern in the development of interconnected and shared networks, given their vulnerability to various attacks. Intrusion detection technology has emerged as a crucial defense mechanism against malicious activities. Traditional methods face limitations in detecting complex intrusion behaviors, leading to a shift towards machine learning-based approaches. Deep learning models like Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM) offer promising solutions by capturing intricate patterns in network traffic data. This paper introduces a fusion network intrusion detection model, leveraging Grey Wolf Optimizer (GWO) and Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM), addressing emerging challenges in network security. The model's efficacy was assessed on the CICIDS2017 dataset and compared against Random Forest and CNN-BiLSTM models. Results confirm the viability and efficacy of the proposed approach, offering a novel strategy for network intrusion detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fan Yu, Bingxin Tian, Hengquan Yu, and Jiuchun Ren "An intrusion detection system based on GWO-CNN-BiLSTM", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132292C (7 August 2024); https://doi.org/10.1117/12.3038081

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