Malware Detection using Convolutional Neural Network

Author(s): Harmeet Thukran, Neeti Kashyap

Abstract

In the Internet-age, malware poses a serious and evolving threat to security, making the detection of malware of utmost concern. Many research efforts have been conducted on intelligent malware detection by applying data mining and machine learning techniques. In this project we considered a portable executable file as an image and used image classification technique to classify any given exe file into malware or benign- ware. We used different feature extraction techniques such as Edge detection, ORB, Log gabor and Gabor filter. We used a pre- trained densenet121 model and achieved a maximum accuracy of 94.04% using just an ORB filter.

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