| Title | Malware Classification Framework Using Convolutional Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Khan, Mamoona, Baig, Duaa, Khan, Usman Shahid, Karim, Ahmad |
| Conference Name | 2020 International Conference on Cyber Warfare and Security (ICCWS) |
| Date Published | oct |
| Keywords | Biological neural networks, convolutional neural network, Data models, Deep Learning, dense neural network, feature extraction, Hidden Markov models, Human Behavior, Malware, malware classication, Metrics, Neural networks, privacy, pubcrawl, resilience, Resiliency |
| Abstract | Cyber-security is facing a huge threat from malware and malware mass production due to its mutation factors. Classification of malware by their features is necessary for the security of information technology (IT) society. To provide security from malware, deep neural networks (DNN) can offer a superior solution for the detection and categorization of malware samples by using image classification techniques. To strengthen our ideology of malware classification through image recognition, we have experimented by comparing two perspectives of malware classification. The first perspective implements dense neural networks on binary files and the other applies deep layered convolutional neural network on malware images. The proposed model is trained to a set of malware samples, which are further distributed into 9 different families. The dataset of malware samples which is used in this paper is provided by Microsoft for Microsoft Malware Classification Challenge in 2015. The proposed model shows an accuracy of 97.80% on the provided dataset. By using the proposed model optimum classifications results can be attained. |
| DOI | 10.1109/ICCWS48432.2020.9292384 |
| Citation Key | khan_malware_2020 |
Malware Classification Framework Using Convolutional Neural Network