CAJ | 학술논문

针对单一分类方法在训练样本不足的情况下对于小样本网络流分类效果差的特点，通过自适应增强（Adaptive Boosting，AdaBoost）算法进行流量分类。算法首先使用CFS（Correlation-based Feature Se-lection）特征选择方法从大量网络流特征中提取出少量高效的分类特征，在此基础上，通过AdaBoost算法组合决策树、关联规则和贝叶斯等5种单一分类方法实现流量分类。实际网络流量数据测试表明，基于AdaBoost的组合分类方法的准确率在所选的几种算法中是最高的，其能够达到98.92％，且相对于单一的分类算法，组合流量分类方法对于小样本网络流的分类效果具有明显提升。
침대단일분류방법재훈련양본불족적정황하대우소양본망락류분류효과차적특점，통과자괄응증강（Adaptive Boosting，AdaBoost）산법진행류량분류。산법수선사용CFS（Correlation-based Feature Se-lection）특정선택방법종대량망락류특정중제취출소량고효적분류특정，재차기출상，통과AdaBoost산법조합결책수、관련규칙화패협사등5충단일분류방법실현류량분류。실제망락류량수거측시표명，기우AdaBoost적조합분류방법적준학솔재소선적궤충산법중시최고적，기능구체도98.92％，차상대우단일적분류산법，조합류량분류방법대우소양본망락류적분류효과구유명현제승。
To cope with the poor performance of single classification algorithms on minority flows when the train dataset is deficient,the AdaBoost (Adaptive Boosting)algorithm is introduced to classify network traffic . On the basis of selecting few but effective classification features with CFS (Correlation-based Feature Selection)method from a variety of flow′s features,the AdaBoost algorithm is used to combine five single classification algorithms which belong to Decision Tree,Rules and Bayes respectively for the sake of traffic classification . The experi-ment over real network traffic shows that the AdaBoost algorithm has the highest precision up to 98.92% amongthe selected classification algorithms . Moreover,the AdaBoost algorithm achieves great improvement on the per-formance of minority flows′ classification compared with single classification algorithms .