Improving malware detection by applying multi-inducer ensemble

摘要: Detection of malicious software (malware) using machine learning methods has been explored extensively to enable fast detection new released malware. The performance these classifiers depends on the induction algorithms being used. In order benefit from multiple different classifiers, and exploit their strengths we suggest an ensemble method that will combine results individual into one final result achieve overall higher accuracy. this paper evaluate several combining five base inducers (C4.5 Decision Tree, Naive Bayes, KNN, VFI OneR) malware datasets. main goal is find best for task detecting files in terms accuracy, AUC Execution time.