| Title | Visual Malware Classification Using Transfer Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Khetarpal, Anavi, Mallik, Abhishek |
| Conference Name | 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) |
| Keywords | Benchmark testing, classification, convolutional neural network, Deep Learning, Electric potential, feature extraction, Gray-scale, Human Behavior, Malware, malware classification, Predictive Metrics, privacy, pubcrawl, Resiliency, transfer learning, visualisation, visualization |
| Abstract | The proliferation of malware attacks causes a hindrance to cybersecurity thus, posing a significant threat to our devices. The variety and number of both known as well as unknown malware makes it difficult to detect it. Research suggests that the ramifications of malware are only becoming worse with time and hence malware analysis becomes crucial. This paper proposes a visual malware classification technique to convert malware executables into their visual representations and obtain grayscale images of malicious files. These grayscale images are then used to classify malicious files into their respective malware families by passing them through deep convolutional neural networks (CNN). As part of deep CNN, we use various ImageNet models and compare their performance. |
| DOI | 10.1109/ICECCT52121.2021.9616822 |
| Citation Key | khetarpal_visual_2021 |
Visual Malware Classification Using Transfer Learning