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Analyzing host security using D‐S evidence theory and multisource information fusion
Author(s) -
Li Yuanzhang,
Yao Shangjun,
Zhang Ruyun,
Yang Chen
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22330
Subject(s) - host (biology) , backup , computer science , data mining , support vector machine , domain (mathematical analysis) , information security , security analysis , computer security , information fusion , logistic regression , sensor fusion , artificial intelligence , machine learning , mathematics , database , ecology , mathematical analysis , biology
Security monitoring and analysis can help users to timely perceive threats faced by the host, thereby protecting and backup data and improving the host's security status. In the research domain of host security analysis, many feasible solutions have been proposed. However, real‐time performance and accuracy still need improvement. This paper proposes a host security analysis method based on Dempster–Shafer (D‐S) evidence theory. It adopts three models of support vector regression, logistic regression, and K‐nearest neighbor regression, as sensors for multisource information fusion. Multiple sensors perform security analysis on the host, respectively, and use the analysis results as evidence of D‐S evidence theory. Experiments show that the proposed method provides effective security protection for the host in terms of absolute error, root mean square error, and the average absolute percentage error.