User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection. (24th February 2020)

• Record Type: Journal Article
• Authors: Ma, Zheng; Ma, Yan; Huang, Xiaohong; Zhang, Manjun; Su, Bo; Zhao, Liang
• Is Part Of: International journal of information and communication technology. Volume 16:Number 2(2020)
• Page Start: 99
• Page End: 111
• Publication Date: 2020-02-24
• Subjects: distributed intelligent computing -- user information -- intrusion prediction -- feature extraction -- empirical mode decomposition; Information technology -- Periodicals; Computer science -- Periodicals; Telecommunication -- Periodicals; 004.05
• Languages: English
• ISSNs: 1466-6642

Abstract

In distributed intelligent computing environment, user information is vulnerable to plaintext intrusion, resulting in information leakage. In order to ensure the security of user information, a user information intrusion prediction method based on empirical mode decomposition and spectrum feature detection in distributed intelligent computing is proposed in this paper. Firstly, a model of user information and intrusion signal in distributed intelligent computing is established; then an intrusion detection model is established with signal processing method; finally, time-frequency analysis and feature decomposition are conducted for intrusion information in distributed intelligent computing with empirical mode decomposition method, and accurate prediction of user intrusion information is achieved based on joint probability density distribution of spectrum feature, so as to improve the algorithm design. The simulation results show that when the signal to noise ratio is 12.4 dB, the detection probability of the method proposed in this paper is 1, and then the false alarm probability can be 0, which indicates that this method can provide good intrusion detection probability and low false alarm probability even at relatively low signal to noise ratio. Therefore, the method proposed in this paper has good intrusion interception and prediction ability.