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| 基于多标记与半监督学习的入侵检测方法研究 | |
| 引用本文： | 钱燕燕,李永忠,余西亚. 基于多标记与半监督学习的入侵检测方法研究[J]. 计算机科学, 2015, 42(2): 134-136,146 |
| 作者姓名： | 钱燕燕 李永忠 余西亚 |
| 作者单位： | 江苏科技大学计算机科学与技术学院 镇江212003 |
| 基金项目： | 本文受江苏省高校自然科学基金项目(05KJD52006),江苏科技大学科研资助 |
| 摘 要： | 机器学习所关注的问题是系统如何随着经验积累自动提高分类性能,这与入侵检测通过对外界入侵进行自我学习来提高其检测率和降低误报率是一致的。因此把机器学习的理论和方法引入到入侵检测中已成为一种有效方案。文中结合多标记与半监督学习理论,将ML-KNN算法应用于入侵检测系统。在KDD CUP99数据集上的仿真结果表明,该方法在入侵检测中能获得高检测率和低误报率。 |
| 关 键 词： | 多标记学习 ML-KNN算法 半监督学习 入侵检测 |
| 收稿时间： | 2014-08-23 |
| 修稿时间： | 2014-09-25 |
Intrusion Detection Method Based on Multi-label and Semi-supervised Learning |
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| QIAN Yan-yan,LI Yong-zhong and YU Xi-ya. Intrusion Detection Method Based on Multi-label and Semi-supervised Learning[J]. Computer Science, 2015, 42(2): 134-136,146 | |
| Authors: | QIAN Yan-yan LI Yong-zhong YU Xi-ya |
| Affiliation: | School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang 212003,China,School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang 212003,China and School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang 212003,China |
| Abstract: | The concerned problem of machine learning is how the systems automatically improve the classification performance with the increase of experience,which is consistent with IDS.Therefore,it has become an effective program to put the theories and methods of machine learning into IDS.In this paper,a multi-label lazy learning approach named ML-KNN was applied to intrusion detection systems.KDD CUP99 data set was implemented to evaluate the ML-KNN algorithm.The simulation results show that this method can achieve higher detection rate and lower false positive rate compared to other algorithms. |
| Keywords: | Multi-label learning ML-KNN algorithm Semi-supervised learning Intrusion detection |
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