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- 2018
一种基于信息论模型的入侵检测特征提取方法
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Abstract:
在网络入侵检测中，由于原始数据特征维度高和冗余特征多，导致入侵检测系统的存储负担增加，检测分类器性能降低。针对该问题本文提出了一种基于信息论模型的入侵检测特征提取方法。它以具有最大信息增益的特征为搜索起点，利用搜索策略和评估函数迭代调整数据集分类标记、已选取特征子集和候选特征三者之间的相关度，最后通过终止条件确定选取特征子集。以入侵检测样本数据集为实验数据，将该方法选取的特征向量运用到支持向量机分类算法中，在特征维度大幅度降低的情况下，检测精度变化很小。实验结果证明了本方法的有效性。
| [1] | QUINLAN J R. Programs for machine learning[M]. San Mateo, CA:Morgan Kaufmann, 1993. |
| [2] | CAI Zhi-ping, WANG Zhi-jun, ZHENG Kai, et al. A distributed TCAM coprocessor architecture for integrated longest prefix matching, policy filtering, and content filtering[J]. IEEE Trans Computers, 2013, 62(3):417-427. |
| [3] | 方峰, 蔡志平, 肇启佳, 等. 使用Spark Streaming的自适应实时DDoS检测和防御技术[J]. 计算机科学与探索, 2016, 10(5):601-611. FANG Feng, CAI Zhi-ping, ZHAO Qi-jia, et al. Adaptive technique for real-time DDoS detection and defense using spark streaming[J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(5):601-611. |
| [4] | CHIMIENTI M,CORNULIER T,OWEN E. The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data[J]. Ecology & Evolution, 2016, 6(3):1948-1952. |
| [5] | 杨国亮, 谢乃俊, 王艳芳, 等. 基于低秩稀疏评分的非监督特征选择[J]. 计算机工程与科学, 2015, 37(4):649-656. YANG Guo-liang, XIE Nai-jun, WANG Yan-fang, et al. Unsupervised feature selection based on low rank and sparse score[J]. Computer Engineering and Science, 2015, 37(4):649-656. |
| [6] | MANSOORI E G, SHAFIEE K S. On fuzzy feature selection in designing fuzzy classifiers for high-dimensional data[J]. Evolving Systems, 2015, 7(4):1-11. |
| [7] | 李玲, 刘华文, 徐晓丹, 等. 基于信息增益的多标签特征选择算法[J]. 计算机科学, 2015, 42(7):52-56. LI Ling, LIU Hua-wen, XU Xiao-dan, et al. Multi-label feature selection algorithm based on information gain[J]. Computer Science, 2015, 42(7):52-56. |
| [8] | WANG H, SUN H B, ZHANG B M. PG-HMI:Mutual information based feature selection method[J]. Pattern Recognition & Artificial Intelligence, 2007, 20(1):55-63. |
| [9] | ]ZHENG Zhao, LIU Huan. Semi-supervised featrue selection via spectral analysis[C]//Proceedings of the 7th SIAM International Conference on Data Mining.[S.l.]:DBLP, 2007:1193-1201. |
| [10] | 史彩娟. 网络空间图像标注中半监督稀疏特征选择算法研究[D]. 北京:北京交通大学, 2015. SHI Cai-juan. Research on semi-supervised sparse feature selection for image annotation in web space[D]. Beijing:Beijing Jiaotong University, 2015. |
| [11] | YU Lei, LU Ling. Featrue selection based on loss-margin of nearest neighborclassification[J]. Pattern Recongnition, 2009, 42(9):1914-1921. |
| [12] | 郑莹斌. 有监督的视觉特征提取算法研究[D]. 上海:复旦大学, 2013. ZHENG Ying-bin. Research on supervised visual feature extraction algorithms[D]. Shanghai:Fudan University, 2013. |
| [13] | 张丽新, 王家钦, 赵雁南, 等. 机器学习中的特征选择[J]. 计算机科学, 2004, 31(11):180-184. ZHANG Li-xin, WANG Jia-qin, ZHAO Yan-nan, et al. Feature selection in machine learining[J]. Computer Science, 2004, 31(11):180-184. |
| [14] | 唐亮, 段建国, 许洪波, 等. 基于互信息最大化的特征选择算法及应用[J]. 计算机工程与应用, 2008, 44(13):130-133. TANG Liang, DUAN Jian-guo, XU Hong-bo, et al. Mutual information maximization based feature selection algorithm in text classification[J]. Computer Engineering and Applications, 2008, 44(13):130-133. |
