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一种基于人工免疫的云存储安全检测方法 被引量:2

Research on Artificial Immune System Based Storage Security Detection
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摘要 近年来信息化趋势愈演愈烈,数据已摇身成为个人、公司、机构等最有价值的资产之一。随着云存储系统普及率逐渐提高,存储安全问题也屡屡出现。传统的用户认证虽然包含证明和验证两个阶段,但却无法保证用户的真实性。本文提出了一种基于人工免疫的云存储安全检测方法,该方法主要是使用元数据二进制建模技术将用户访问文件请求转化为二进制字符串,采用阴性选择算法生成适当数量的有效检测器,使用这些有效检测器集合判断用户的访问请求是否合法,对非法访问请求予以拦截。实验表明本方法能够在检出率和误报率这两个功能指标方面取得较好的效果。 Data is getting more and more important in the current society.With the gradual increase in cloud storage system,storage security problems have repeatedly appeared.Although traditional storage safety certification system includes identification and authentication,but can not ensure the authenticity of user s identity.This paper proposes a method of distributed storage security detection based on artificial immune.This method converts user access request into a binary string using metadata modeling technique,and generate an appropriate number of valid detectors using the negative selection algorithm,use these valid detectors to detect user access request,intercept the illegal access request.Experimental results show that this method can achieve good results in both detection rate and false alarm rate.
作者 蔡刚山 周刚 CAI Gang-shan;ZHOU Gang(Wuhan Engineering and Science Technology Institute,Wuhan,Hubei 430019,China)
出处 《计算技术与自动化》 2018年第1期107-111,共5页 Computing Technology and Automation
基金 科技部863计划项目(2012AA012904)
关键词 存储安全 人工免疫 元数据 阴性选择算法 storage security artificial immune metadata,negative selection algorithm
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  • 1戴汝为,王珏.关于智能系统的综合集成[J].科学通报,1993,38(14):1249-1256. 被引量:52
  • 2Pennington A G,Strunk J D, Griffin J L, et al. Storage-based Intrusion Detection: Watching storage activity for suspicious be havior[C]//Proeeedings of the 12th USENIX Security Symposium. 2003 : 137-151.
  • 3C-opal R K, Meher S K, A Rule-based Approach for Anomaly Detection in Subscriber Usage Patter[J]. Int. J. of Mathematical,Physical and Engineering Sciences,2007,1(3) : 171-174.
  • 4Qayyum A, Islam M H, Jamil M. Taxonomy of Statistical-based Anomaly Detection Techniques for Intrusion Detection[C]// Proceedings of the IEEE Conference on Emerging Technologies (ICET'05). 2005 : 270-276.
  • 5Durgin N A, Zhang P C. Profile-based Adaptive Anomaly Detection for Network Security[R]. SAND2005 7293. 2005.
  • 6Sekar R, Oupta A, et al. Specification-based anomaly detection : a new approach for detecting network intrusions[C]//9th ACM Conference on Computer and Comm. Security. 2002:265-274.
  • 7Du Y,Wang H Q,Pang Y G. A Hidden Markov models-based Anomaly Intrusion Detection Method[C]//Proceeding of WCICA'04.2004:4348-4351.
  • 8De Castro L N, Von Zuben F J. Artificial Immune Systems: Part I-Basic Theory and Applieations[R]. RT DCA 01/99. 1999:1-95.
  • 9Forrest S, Perelson A S, Allen L, et al. Self-nonself Discrimination in a Computer[C]//Proceedings of the 1994 IEEE Symposium on Security and Privacy. Los Alamitos, CA, 1994 : 202-212.
  • 10Kim J, Bentley P J. Towards an Artificial Immune System for Network Intrusion Detection:An Investigation of Dynamic Clonal Selection[C]//Proceeding of Congress on Evolutionary Computation. 2002 : 1015-1020.

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