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Computers & Security
Volume 24, Issue 3, May 2005, Pages 218-231
 
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doi:10.1016/j.cose.2004.08.009    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2004 Elsevier Ltd All rights reserved.

Multiple behavior information fusion based quantitative threat evaluationstar, open

Xiu-Zhen Chena, Corresponding Author Contact Information, E-mail The Corresponding Author, Qing-Hua Zhenga, E-mail The Corresponding Author, Xiao-Hong Guana, b, E-mail The Corresponding Author, Chen-Guang Lina, E-mail The Corresponding Author and Jie Suna, E-mail The Corresponding Author

aCenter for Networked Systems and Information Security (CNSIS) and SKLMS Lab, Xi'an Jiaotong University, Xian 710049, China bCenter for Intelligent and Networked Systems, Tsinghua University, Beijing 100084, China

Received 27 May 2004; 
revised 11 August 2004; 
accepted 11 August 2004. 
Available online 17 November 2004.

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Abstract

How to evaluate network security threat quantitatively is one of key issues in the field of network security, which is vital for administrators to make decision on the security of computer networks. A novel model of security threat evaluation with a series of quantitative indices is proposed on the analysis of prevalent network intrusions. This model is based on multiple behavior information fusion and two indices of privilege validity and service availability that are proposed to evaluate the impact of prevalent network intrusions on system security, so as to provide security evolution over time, i.e., monitor security changes with respect to modification of security factors. The Markov model and the algorithm of D-S evidence reasoning are proposed to measure these two indices, respectively. Compared with other methods, this method mitigates the impact of unsuccessful intrusions on threat evaluation. It evaluates the impact of important intrusions on system security comprehensively and helps administrators to insight into intrusion steps, determine security state and identify dangerous intrusion traces. Testing in a real network environment shows that this method is reasonable and feasible in alleviating the tremendous task of data analysis and facilitating the understanding of the security evolution of the system for its administrators.

Keywords: Network security; Information fusion; Threat evaluation; Evaluation index; Markov model; Evidence reasoning

Article Outline

Introduction
Related work
MBIF based threat evaluation
Definitions
Model of security threat evaluation based on MBIF
Approach to quantitative evaluation of O1
Algorithm for the reconstruction of intrusion traces
Markov model for quantitative security evaluation
D-S evidence reasoning approach to quantitative evaluation of O2
I. Network bandwidth consumption
II. Packet drop rate
III. Response time delay
Experiments and discussion
Experiment 1 on Red Hat Linux 6.2 server
Experiment 2 on Red Hat Linux 7.2 server
Conclusion and future work
Acknowledgements
References
Vitae






















Computers & Security
Volume 24, Issue 3, May 2005, Pages 218-231
 
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