期刊文献+

Linux下基于SVM分类器的WebShell检测方法研究 被引量:21

Research of Linux WebShell Detection based on SVM Classifier
下载PDF
导出
摘要 WebShell是一种常见的网页后门,它常常被攻击者用来获取Web服务器的操作权限。文章首先分析了Linux下WebShell的实现机理,描述了WebShell的常见特征和特征混淆方法,然后以此为基础,提出了一种基于SVM分类器的检测方法,并在仿真平台下对其予以实现。文章从准确度、特定度和灵敏度3个方面比较了基于SVM分类器的WebShell检测方法、基于特征匹配的WebShell检测方法和基于决策树的WebShell检测方法。实验结果表明,文章提出的方法能够准确、高效地对WebShell进行检测。 WebShell is a common webpage back door, which can be used by attackers to obtain Web server permissions. The realization mechanism of Linux WebShell is analyzed, the common characteristics and the characteristic mixed method are described in this paper. On this basis, a detection method based on SVM classifier is put forward and realized. From three aspects of accuracy, specificity and sensitivity, the WebShell detection methods individually based on SVM classifier, characteristic matching and decision tree are compared. The experimental result shows that the method proposed in this paper can detect WebShell accurately and efficiently.
出处 《信息网络安全》 2014年第5期5-9,共5页 Netinfo Security
基金 国家自然科学基金[61170282]
关键词 WebShell检测 SVM分类器 特征提取 WebShell detection SVM classifier characteristic extraction
  • 相关文献

参考文献16

  • 1胡建康,徐震,马多贺,杨婧.基于决策树的Webshell检测方法研究[J].网络新媒体技术,2012,1(6):15-19. 被引量:28
  • 2袁勋,吴秀清,洪日昌,宋彦,华先胜.基于主动学习SVM分类器的视频分类[J].中国科学技术大学学报,2009,39(5):473-478. 被引量:21
  • 3Xiao Yao. Large and Medium-sized Network Intrusions Cases Research[J]. Publishing House Of Electronics Industry, 2010,(10):301-310.
  • 4J. Ross Quinlan. C4. 5: programs for machine learning[M]. San Francisco: Morgan Kaufmann, 1993.
  • 5Yung-Tsung Hou, Yimeng Chang, Tsuhan Chen.Malicious web content detection by machine learning[J]. Expert Systems with Applications,2010,37(1):55-60.
  • 6Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines[C]//Proceedings of IEEE Workshop on Neural Networks for Signal Processing. Amelia Island, USA: IEEE Press, 1997: 276-285.
  • 7Lin H T, Lin C J, Weng R C. A note on Plat tps probabilistic outputs for support vector machines[J]. Machine Learning, 2007, 68 (3): 267-276.
  • 8Brinker K. On multiclass active learning with support vector machines[C]//Proceedings of European Conference on Artificial Intelligence. 2004: 969-970.
  • 9Yuan X, Lai W, Mei T , et al. Automatic video genre categorization using hierarchical SVM[C]//IEEE International Conference on Image Processing. Atlanta: IEEE Press, 2006: 2905-2908.
  • 10Tong S , Chang. E Support vector machine active learning for image ret rieval[C]//Proceedings of the 9th ACM International Conference on Multimedia. Ottawa, Canada: ACM Press, 2001, 9: 107-118.

二级参考文献29

  • 1张千里.CCERT的建议和入侵检测系统的研究[M].北京:清华大学,2000..
  • 2Tong S. Active learning: theory and applications[D]. Ph. D. dissertation, Stanford University, 2001.
  • 3Brinker K. On multiclass active learning with support vector machines [C]// Proceedings of European Conference on Artificial Intelligence. 2004: 969-970.
  • 4Yan R, Yang J, Hauptmann A. Automatically labeling video data using multi-class active learning [C]// Proceedings of the 9th IEEE International Conference on Computer Vision. Washington: IEEE Computer Society, 2003, 1: 516-523.
  • 5Zhang H J, Kankanhallli A, Smoliar S W. Automatic partitioning of full-motion video [J]. Multimedia Systems, 1993, 1(1). pp: 10-28.
  • 6Lan D J, Ma Y F, Zhang H J. A novel motion-based representation for video mining[C]// Proceedings of IEEE International Conference on Multimedia & Expo. Washington: IEEE Computer Society, 2003: 469-472.
  • 7Vapnik V. Statistical Learning Theory [M]. New York: Wiley, 1998.
  • 8Wu T F, Lin C H, Weng R C. Probability estimates for multi-class classification by pairwise coupling[J]. The Journal of Machine Learning Research. 2004, 5: 975-1005.
  • 9Lin H T, Lin C J, Weng R C. A note on Platt's probabilistic outputs for support vector machines[J]. Machine Learning, 2007, 68(3):267-276.
  • 10Osuna E, Freund R, Girosi K An improved training algorithm for support vector machines[C]//Proceedings of IEEE Workshop on Neural Networks for Signal Processing Amelia Island, USA: IEEE Press, 1997: 276-285.

共引文献241

同被引文献103

引证文献21

二级引证文献101

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部