Polymorphic Malware Detection by Image Conversion Technique

Abstract

This model implements ways to detect polymorphic malware. This model uses a dynamic approach to detect the polymorphic malware. The objective is to increase the accuracy and efficiency of the detection as this malware can morph themselves, making it difficult to trace through anti-malware systems. As the tracing is going to be difficult the detection and classification system needs to be flexible that can able to detect the malware in every possible environment. This objective can be achieved by giving the system a superintelligence, this can be done by using the Convolutional Neural Networks (CNNs) in our system. This method records the pattern or the traces made by the polymorphic malware. The pattern is in the form of the image which is formed by converting the binary format of the hash codes. The generated images are then sent to the training module, based on this training module the Convolutional Neural Networks gives the result for any testing data.