Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019

Research Article

PSVM: Quantitative Analysis Method of Intelligent System Risk in Independent Host Environment

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  • @INPROCEEDINGS{10.1007/978-3-030-48513-9_43,
        author={Shanming Wei and Haiyuan Shen and Qianmu Li and Mahardhika Pratama and Meng Shunmei and Huaqiu Long and Yi Xia},
        title={PSVM: Quantitative Analysis Method of Intelligent System Risk in Independent Host Environment},
        proceedings={Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019},
        proceedings_a={CLOUDCOMP},
        year={2020},
        month={6},
        keywords={DT-SVM PSO-SVM Ubiquitous network},
        doi={10.1007/978-3-030-48513-9_43}
    }
    
  • Shanming Wei
    Haiyuan Shen
    Qianmu Li
    Mahardhika Pratama
    Meng Shunmei
    Huaqiu Long
    Yi Xia
    Year: 2020
    PSVM: Quantitative Analysis Method of Intelligent System Risk in Independent Host Environment
    CLOUDCOMP
    Springer
    DOI: 10.1007/978-3-030-48513-9_43
Shanming Wei, Haiyuan Shen1, Qianmu Li2,*, Mahardhika Pratama3, Meng Shunmei2, Huaqiu Long4, Yi Xia5
  • 1: Jiangsu Zhongtian Technology Co., Ltd.
  • 2: Nanjing University of Science and Technology
  • 3: Nanyang Technological University
  • 4: Wuyi University
  • 5: PT. Sinoma Engineering Indonesia
*Contact email: qianmu@njust.edu.cn

Abstract

Quantitative risk analysis of security incidents is a typical non-linear classification problem under limited samples. Having advantages of strong generalization ability and fast learning speed, the Support Vector Machine (SVM) is able to solve classification problems in limited samples. To solve the problem of multi-classification, Decision Tree Support Vector Machine (DT-SVM) algorithm is used to construct multi-classifier to reduce the number of classifiers and eliminate non-partitionable regions. Particle Swarm Optimization (PSO) algorithm is introduced to cluster training samples to improve the classification accuracy of the constructed multi-classifier. In the ubiquitous network, the cost of information extraction and processing is significantly lower than that of traditional networks. This paper presents a quantitative analysis method of security risk based on Particle Swarm Optimization Support Vector Machine (PSO-SVM), and classifies the flow data by combining the way of obtaining the flow data in ubiquitous networks, so as to realize the quantitative analysis of the security risk in ubiquitous networks.