End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware
This addresses the challenge of attributing advanced persistent threats for cybersecurity analysts, though it is incremental as it applies existing deep learning methods to a new domain with small datasets.
The paper tackled the problem of attributing nation-state malware to specific countries or families by using a deep neural network to classify APTs based on dynamic behavior, achieving 98.6% accuracy on a test set of 1000 Chinese and Russian APTs.
Malware allegedly developed by nation-states, also known as advanced persistent threats (APT), are becoming more common. The task of attributing an APT to a specific nation-state or classifying it to the correct APT family is challenging for several reasons. First, each nation-state has more than a single cyber unit that develops such malware, rendering traditional authorship attribution algorithms useless. Furthermore, the dataset of such available APTs is still extremely small. Finally, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. In this paper, we use a deep neural network (DNN) as a classifier for nation-state APT attribution. We record the dynamic behavior of the APT when run in a sandbox and use it as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. We also use the same raw features for APT family classification. Finally, we use the feature abstractions learned by the APT family classifier to solve the attribution problem. Using a test set of 1000 Chinese and Russian developed APTs, we achieved an accuracy rate of 98.6%.