The use of Convolutional Neural Network for Malware Classification presented at CNSA 2020

by Shah Rukh Sajjad, Bi Jiana, Shah Zaib Sajjad,

Summary : Digital security is confronting an immense risk from malwares or malicious software. In recent years, there has been an increase in the volume of malwares, reaching above 980 million in 2019*. To identify and classify these pernicious software, complex details and patterns among them are to be gathered, segregated and analyzed. In this regard, Convolutional Neural Networks (CNN) – an architecture of Deep Neural Networks (DDN) can offer a more efficient and accurate solution than conventional neural network (NN) systems. In this paper, we have looked into the consequences of using conventional NN systems and benefits of using CNN on a sample of malwares provided by Microsoft. In 2015, Microsoft announced a malware classification challenge and released more than 21,000 malware samples. Many interesting solutions were put forward by scientists and students around the world. Inspired by their efforts we also have put forward a method. We converted the malware binary files into images and then trained a CNN model for identification and categorization of these malwares to their respective families. From this method, we achieved a high percentage accuracy of 98.80%.