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Automated Malware Classification based on Hybrid CNN-SVM framework

Ishan Jain, Srishti Vashishtha, Deepika Varshney, Rachna Narula, Vinayak Sharma

Abstract


Research is being conducted on malware analysis and classification using various models and techniques. The increasing presence of malware code files has made manual analysis time-consuming, so efficient tools are needed to quickly detect malware. One popular technique is using machine learning models to classify malware code as images, which simplifies the detection process. The objective is to train a model that can classify new malware files on its own, using techniques such as CNNs for image processing and subsequent classification. This paper proposes the usage of a CNN + SVM model for classification which is shown to outperform popular classification methodologies.


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References


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