Intrusion Detection System Using Voting-Based Neural Network

作     者：Mohammad Hashem Haghighat Jun Li Mohammad Hashem Haghighat;Jun Li

作者机构：Department of AutomationTsinghua UniversityBeijing 100084China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2021年第26卷第4期

页      面：484-495页

核心收录：

学科分类：08[工学] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 

基　　金：supported by the National Natural Science Foundation of China (No. 61872212) the National Key Research and Development Program of China (No.2016YFB1000102)。 

主　　题：deep learning Voting-based Neural Network(VNN) network security Pearson correlation coefficient 

摘      要：Several security solutions have been proposed to detect network abnormal behavior. However, successful attacks is still a big concern in computer society. Lots of security breaches, like Distributed Denial of Service(DDoS),botnets, spam, phishing, and so on, are reported every day, while the number of attacks are still increasing. In this paper, a novel voting-based deep learning framework, called VNN, is proposed to take the advantage of any kinds of deep learning structures. Considering several models created by different aspects of data and various deep learning structures, VNN provides the ability to aggregate the best models in order to create more accurate and robust results. Therefore, VNN helps the security specialists to detect more complicated attacks. Experimental results over KDDCUP 99 and CTU-13, as two well known and more widely employed datasets in computer network area, revealed the voting procedure was highly effective to increase the system performance, where the false alarms were reduced up to 75% in comparison with the original deep learning models, including Deep Neural Network(DNN), Convolutional Neural Network(CNN), Long Short-Term Memory(LSTM), and Gated Recurrent Unit(GRU).