DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks

Abstract

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%.

References in corpus (4)

• Natural Language Processing (almost) from Scratch
• DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
• When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries
• On the Feasibility of Malware Authorship Attribution

Cited by in corpus (5)

• Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework
• Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference
• End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware
• Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
• Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data