End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware

TL;DR

This paper presents a deep neural network approach that analyzes sandbox-recorded behaviors of nation-state malware to accurately attribute APTs to their origin with high precision, even with limited data and evasion techniques.

Contribution

It introduces a novel end-to-end DNN framework that leverages dynamic behavior features for nation-state APT attribution and family classification, improving accuracy over traditional methods.

Findings

Achieved 98.6% accuracy in attributing APTs to China and Russia.

Utilized raw sandbox behavior data for effective feature learning.

Demonstrated the effectiveness of transfer learning from family classification to attribution.

Abstract

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%.