Neural Network Collation: A Comparative Study on Novel Image-based Malware Classification through Neural Network
Keywords:
Deep Learning, Neural Network, Convolution Neural Network, Capsule Neural Network, Feature engineeringAbstract
The term malware is defined as any malicious software that affects the system or software. A malware is a piece of program that sticks to the system and affects the same. Most of the times, it is found stealthy and infects the user without his knowledge. But this malware can be benign and the classification of the malware from benign needs to identified. Several algorithms come in hand in detecting the malware like KNN, SVM, and decision tree. This comparative study up brings the various malware classification methods and identifies the one with the best accuracy. The work portrays two classification algorithms Such as Mal_CNN and Mal_CapsNet by the author along with the standard CNN and Capsule Neural Network. The work delves by augmenting the Malimg dataset of 9339 malware with 25 malware families for training the model. With this result, segmentation is worked upon to produce 27890 images. With the resultant image, the work flows upon the Mal_CNN and Mal_CapsNet to produce a greater accuracy. After several experiments on the pretrained model, it is found that Mal_CapsNet achieves a significant accuracy of 97.6%. The study focuses a comparison on the four models like CNN, Capsule Neural Network, Mal_CNN and Mal_CapsNet, to identify the best model for malware classification.
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