An adaptive LeNet-5 model for anomaly detection. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Cui, Wenchao; Lu, Qiong; Qureshi, Asif Moin; Li, Wei; Wu, Kehe
• Is Part Of: Information security journal. Volume 30:Issue 1(2021)
• Page Start: 19
• Page End: 29
• Publication Date: 2021-01-02
• Subjects: Anomaly detection -- attack classification -- CICIDS2017 dataset -- convolutional neural network -- LeNet-5 network; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1080/19393555.2020.1797248
• Languages: English
• ISSNs: 1939-3555

Abstract

ABSTRACT: This paper introduced a feature selection algorithm, used a random forest classifier for recursive feature elimination, and selected the top 49 features according to the order of feature importance ranking, then proposed an attack detection model based on LeNet-5 convolutional neural network and named it as LeNet-4 network model. In its network structure, the first pooling layer and the last fully connection layer of the original LeNet-5 network were removed, which reduced the model's computational load and network complexity, and the network self-learning ability was strengthened through the structure of the double convolutional layer and the single pooling layer. This paper used the CICIDS2017 dataset to evaluate the proposed model, we used all instances in the dataset for comprehensive detection. The experimental results show that on the LeNet-4 network model, the introduction of a recursive feature elimination algorithm has improved the accuracy of attack detection while reducing the time cost. Multi-class attack classification achieved an accuracy rate of 97.8%, and the binary attack classification achieved an accuracy rate of 98.5%.