DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks
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%.
Code (0)
등록된 구현이 없습니다.
Tasks
Authorship AttributionSimilar Papers 제목 키워드 기반
Attributional Robustness Training using Input-Gradient Spatial Alignment
Interpretability is an emerging area of research in trustworthy machine learning. Safe deployment of machine learning system mandates that the prediction and its explanation be reliable and robust. Recently, it has been …
BIG-bench Machine LearningObject LocalizationTripletWeakly-Supervised Object LocalizationDistribution-Based Feature Attribution for Explaining the Predictions of Any Classifier
The proliferation of complex, black-box AI models has intensified the need for techniques that can explain their decisions. Feature attribution methods have become a popular solution for providing post-hoc explanations, …
Estimating the Adversarial Robustness of Attributions in Text with Transformers
Explanations are crucial parts of deep neural network (DNN) classifiers. In high stakes applications, faithful and robust explanations are important to understand and gain trust in DNN classifiers. However, recent work h…
Adversarial Robustnesstext-classificationText Classificationtext similarityEvaluating Evidence Attribution in Generated Fact Checking Explanations
Automated fact-checking systems often struggle with trustworthiness, as their generated explanations can include hallucinations. In this work, we explore evidence attribution for fact-checking explanation generation. We …
Explanation GenerationFact CheckingEfficient GNN Explanation via Learning Removal-based Attribution
As Graph Neural Networks (GNNs) have been widely used in real-world applications, model explanations are required not only by users but also by legal regulations. However, simultaneously achieving high fidelity and low c…