在惡意程式分析這領域，雖然近幾年在機器學習與人工智慧的挹注下有顯著的分析成果，然而，一般機器學習的分類方法遇到大量特徵時，會有學習時間過長以及大量消耗資源的問題。 本論文提出一個稱為RFpS（Random Forest predicated Svm）的兩段監督式集成學習的快速分類技術。克服以往因過多的多餘特徵訊息所造成的模型過度配適（overfitting）以及預測雜訊的問題。RFpS是結合Random Forest特徵萃取與SVM強分類的學習與預測能力，針對惡意程式進行快速及精準的分類。驗證的結果說明，RFpS方法與單獨只用SVM比較下，其平均學習塑型速度增加約4.5倍，而預測速度增加約2.5倍，平均精準度提昇約20%，達到98.4%。 As we know some fundamental issues of data mining applications are much more critical and severe once it refers to malware analysis, and unfortunately, they are still not well-addressed. In this paper, the proposed a function, as well as uses supervised feature projection for redundant feature reduction and noise filtering. Combining Random Forest with SVM for named RFPS (Random Forest Predicated Svm), Method of reducing feature and fast classification. The results that the learning time about 4.5 times compared with the SVM , predicted speed increases by about 2.5 times ,and the accuracy is about 20% to 98.4%.
