Network security and user abnormal behavior detection by using deep neural network

Abstract

In the big data network environment, because traditional user abnormal behavior detection methods cannot meet the needs of massive data detection, it cannot quickly respond to constantly updated abnormal behaviors and malware, and does not consider user behavior management. The accuracy of abnormal detection and stability are insufficient. By combining with network traffic analysis technology, this paper propose a custom user abnormal behavior detection model based on deep neural networks which implements fine‐grained analysis of network traffic and customizes user behavior management settings to enable user abnormal detection to meet the requirements of specific network environments. The custom user abnormal behavior detection model uses the data of network traffic analysis as the input vector of the deep neural network algorithm to detect unknown abnormal behaviors in massive data and implement custom user behavior management. Experimental results show that the proposed method has high accuracy, can effectively implement custom user behavior management, and overcomes the shortcomings of traditional user abnormal behavior detection methods.