Procedia Computer Science · 2018 · 24 citations · 2 references
ComputingComputer ScienceCybersecurityFeature EngineeringMachine LearningMalware AnalysisBehavioral AnalysisDeep LearningMalware ClassificationPattern RecognitionSequence Pattern RecognitionTime Series Feature ExtractionFeature Extraction For Malware AnalysisFeature Extraction For Malware ClassificationMachine Learning ClassificationMachine Learning-based Malware DetectionMalware Behavior AnalysisMalware DetectionMalware Feature ExtractionStatistical Feature Extraction
Dynamic analysis of malware sample is an important method in the malware detection. In this paper, a malware detection architecture is proposed that combines machine learning and deep learning. The combination classification architecture focuses on the dynamic behavior of a malware sample. A new feature extraction method is proposed for dynamic behavior analysis. In this paper, recurrent neural network model is used to extracts the abstract features. Several sequence data preprocessing methods are studied to remove the redundant data. Experiments show that the AUC of the combination architecture is 99.3%. The classification performance of the combination architecture is better than the separate machine learning or deep learning.
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