J4 ›› 2013, Vol. 40 ›› Issue (1): 44-47+154.doi: 10.3969/j.issn.1001-2400.2013.01.008

• 研究论文 • 上一篇    下一篇



  1. (西安电子科技大学 计算机学院,陕西 西安  710071)
  • 收稿日期:2011-10-04 出版日期:2013-02-20 发布日期:2013-03-28
  • 通讯作者: 吴德
  • 作者简介:吴德(1979-),男,西安电子科技大学博士研究生,E-mail: jump_wude@163.com.
  • 基金资助:


Risk assessment model of information security SVRAMIS

WU De;LIU Sanyang   

  1. (School of Computer Science and Technology, Xidian Univ., Xi'an  710071, China)
  • Received:2011-10-04 Online:2013-02-20 Published:2013-03-28
  • Contact: WU De


针对信息安全风险评估存在风险等级划分不准确和评估时间较长的问题,提出了一种基于支持向量域描述的信息安全风险评估模型.首先,运用支持向量域描述求得各类信息安全样本的最小包围超球,并通过描述边界对样本进行划分; 其次,根据超球球心、半径与样本提供的信息,判断待测样本的空间位置,并实现相应的判别准则. 信息安全数据上的数值试验表明,对不同的核函数,该模型均能具有较高的训练、较高的测试精度以及较短的训练时间.

关键词: 信息安全, 风险评估, 支持向量域描述, 最小包围超球, 空间位置


To solve the problems in information security risk assessment, such as inaccurate security classification and long assessment time, a risk assessment model of information security (RAMIS) is proposed based on Support Vector Domain Description (SVDD), and is called SVRAMIS for short. Firstly, SVDD is applied to obtain the minimal enclosing ball (MEB) of each class, and disconnect regions are obtained by the description boundary. Secondly, based on the information provided by the hypersphere centers and the hypersphere radius, the positions of the test samples are confirmed, so that corresponding discrimination rules can be adopted. Finally, numerical experiments on information security data demonstrate that, for various kernel functions, the proposed model can lead to high training and testing accuracies and short training time.

Key words: information security, risk assessment, SVDD, MEB, space position


  • TP393