Malicious Code Detection Based on Image Processing Using Deep Learning
| Title | Malicious Code Detection Based on Image Processing Using Deep Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Kumar, Rajesh, Xiaosong, Zhang, Khan, Riaz Ullah, Ahad, Ijaz, Kumar, Jay |
| Conference Name | Proceedings of the 2018 International Conference on Computing and Artificial Intelligence |
| Date Published | March 2018 |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6419-5 |
| Keywords | Artificial neural networks, Collaboration, convolutional neural network, cyber physical systems, Deep Learning, Mal- ware Classification, malware detection, Metrics, policy-based governance, pubcrawl, Resiliency |
| Abstract | In this study, we have used the Image Similarity technique to detect the unknown or new type of malware using CNN ap- proach. CNN was investigated and tested with three types of datasets i.e. one from Vision Research Lab, which contains 9458 gray-scale images that have been extracted from the same number of malware samples that come from 25 differ- ent malware families, and second was benign dataset which contained 3000 different kinds of benign software. Benign dataset and dataset vision research lab were initially exe- cutable files which were converted in to binary code and then converted in to image files. We obtained a testing ac- curacy of 98% on Vision Research dataset. |
| URL | https://dl.acm.org/doi/10.1145/3194452.3194459 |
| DOI | 10.1145/3194452.3194459 |
| Citation Key | kumar_malicious_2018 |