Malware Classification Using Transfer Learning
This work addresses the need for efficient malware detection to protect Internet devices, though it is incremental as it builds on existing transfer learning methods.
The paper tackled the problem of rapid malware classification by applying transfer learning with pre-trained deep network architectures on malware images, achieving accurate classification with very short training periods.
With the rapid growth of the number of devices on the Internet, malware poses a threat not only to the affected devices but also their ability to use said devices to launch attacks on the Internet ecosystem. Rapid malware classification is an important tools to combat that threat. One of the successful approaches to classification is based on malware images and deep learning. While many deep learning architectures are very accurate they usually take a long time to train. In this work we perform experiments on multiple well known, pre-trained, deep network architectures in the context of transfer learning. We show that almost all them classify malware accurately with a very short training period.