DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks
This addresses the challenge of nation-state APT attribution for cybersecurity analysts, though it appears incremental as it applies existing deep learning methods to a new domain with small datasets.
The paper tackled the problem of attributing advanced persistent threats (APT) to specific nation-states by using deep neural networks on sandbox reports, achieving an accuracy of 94.6% on a test set of 1,000 APTs.
In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. Finally, the dataset of such available APTs is extremely small. In this paper we describe how deep neural networks (DNN) could be successfully employed for nation-state APT attribution. We use sandbox reports (recording the behavior of the APT when run dynamically) as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs, we achieved an accuracy rate of 94.6%.