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Journal of Computer Networks and Communications
Volume 2016 (2016), Article ID 4517019, 10 pages
Research Article

A Quantitative Risk Evaluation Model for Network Security Based on Body Temperature

School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan 450002, China

Received 8 April 2016; Revised 29 June 2016; Accepted 20 July 2016

Academic Editor: Tzonelih Hwang

Copyright © 2016 Y. P. Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


These days, in allusion to the traditional network security risk evaluation model, which have certain limitations for real-time, accuracy, characterization. This paper proposed a quantitative risk evaluation model for network security based on body temperature (QREM-BT), which refers to the mechanism of biological immune system and the imbalance of immune system which can result in body temperature changes, firstly, through the -contiguous bits nonconstant matching rate algorithm to improve the detection quality of detector and reduce missing rate or false detection rate. Then the dynamic evolution process of the detector was described in detail. And the mechanism of increased antibody concentration, which is made up of activating mature detector and cloning memory detector, is mainly used to assess network risk caused by various species of attacks. Based on these reasons, this paper not only established the equation of antibody concentration increase factor but also put forward the antibody concentration quantitative calculation model. Finally, because the mechanism of antibody concentration change is reasonable and effective, which can effectively reflect the network risk, thus body temperature evaluation model was established in this paper. The simulation results showed that, according to body temperature value, the proposed model has more effective, real time to assess network security risk.