Open AccessIntelligent detection system of asset security vulnerability hidden danger under multiple and heterogeneous Web network
Author(s) -
Yonggang Li,
Jie Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1684/1/012005
Subject(s) - computer science , crawling , web crawler , vulnerability (computing) , asset (computer security) , computer security , vulnerability management , network security , security information and event management , vulnerability assessment , cloud computing security , world wide web , cloud computing , medicine , psychology , psychological resilience , psychotherapist , anatomy , operating system
—Traditional network security vulnerability detection system is affected by detection tools, and the detection accuracy of complex network vulnerability is low. Therefore, this paper designs an intelligent detection system of asset security vulnerabilities under the multi heterogeneous web network. By crawling the asset security data in web network by crawler, the crawling data is integrated by using the characteristics of multi heterogeneous network. According to different types of security vulnerabilities, vulnerability detection is carried out to realize the intelligent detection of network asset security vulnerabilities. In order to verify the detection effect of system security vulnerabilities, comparative experiments are designed. The results show that the average false alarm rate of the designed system is only 12.3%, which is nearly 40% lower than that of the traditional system, which effectively improves the vulnerability detection accuracy.