基于主成分分析和多分类器融合的入侵检测
Intrusion Detection Methods Based on PCA and Multi-Classifier Fusion
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摘要: 提出了利用主成分分析 (PCA) 提取入侵特征的多分类器融合的入侵检测算法.首先, 利用PCA分类提取入侵子特征, 然后通过KNN分类器给出初步的识别结果, 最后采用D-S证据理论对识别结果进行融合, 得出最终识别结果.通过在KDDCUP’99的标准入侵检测数据集上的实验表明, 该方法提高了入侵检测的整体性能.Abstract: An intrusion detection method using multi-classifier fusion based on Principal Component Analysis (PCA) feature analysis is presented. Firstly, PCA is applied to network intrusion feature extraction. then, initial intrusion detection result is done by three KNN classifiers. Next, the last step is form final result by fusing these results using the D-S evidence theory. Experiment has been done on dataset in KDD-99 and the results show that the intrusion detection performances can be improved by fusion three classifiers.
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