Visible to the public A Novel Approach Based on Lightweight Deep Neural Network for Network Intrusion Detection

TitleA Novel Approach Based on Lightweight Deep Neural Network for Network Intrusion Detection
Publication TypeConference Paper
Year of Publication2021
AuthorsZhao, Ruijie, Li, Zhaojie, Xue, Zhi, Ohtsuki, Tomoaki, Gui, Guan
Conference Name2021 IEEE Wireless Communications and Networking Conference (WCNC)
Date Publishedmar
Keywordscomposability, Computational modeling, Data preprocessing, Deep Learning, Intrusion detection, lightweight neural network, Metrics, network applications, network intrusion detection, Neural Network Security, Neural networks, pubcrawl, resilience, Resiliency, Training
AbstractWith the ubiquitous network applications and the continuous development of network attack technology, all social circles have paid close attention to the cyberspace security. Intrusion detection systems (IDS) plays a very important role in ensuring computer and communication systems security. Recently, deep learning has achieved a great success in the field of intrusion detection. However, the high computational complexity poses a major hurdle for the practical deployment of DL-based models. In this paper, we propose a novel approach based on a lightweight deep neural network (LNN) for IDS. We design a lightweight unit that can fully extract data features while reducing the computational burden by expanding and compressing feature maps. In addition, we use inverse residual structure and channel shuffle operation to achieve more effective training. Experiment results show that our proposed model for intrusion detection not only reduces the computational cost by 61.99% and the model size by 58.84%, but also achieves satisfactory accuracy and detection rate.
DOI10.1109/WCNC49053.2021.9417568
Citation Keyzhao_novel_2021