Title: User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection

Authors: Zheng Ma; Yan Ma; Xiaohong Huang; Manjun Zhang; Bo Su; Liang Zhao

Addresses: Beijing University of Posts and Telecommunications, Beijing 100876, China; Network Technology Research Institute, China United Network Communications Co., Ltd., Beijing 100048, China ' Beijing University of Posts and Telecommunications, Beijing 100876, China ' Beijing University of Posts and Telecommunications, Beijing 100876, China ' Network Technology Research Institute, China United Network Communications Co., Ltd., Beijing 100048, China ' College of Aerospace Science and Technology, Xidian University, Xi'an 710000, China ' Science and Technology on Space Physics Laboratory, Beijing, China

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.

Keywords: distributed intelligent computing; user information; intrusion prediction; feature extraction; empirical mode decomposition.

DOI: 10.1504/IJICT.2020.105602

International Journal of Information and Communication Technology, 2020 Vol.16 No.2, pp.99 - 111

Received: 14 Nov 2018
Accepted: 08 Dec 2018
Published online: 06 Mar 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article