用贝叶斯视觉变压器提升恶意软件标注效率，仅需少量标注即可识别百万级未知变种。
A Novel Active Learning Approach to Label One Million Unknown Malware Variants
- 基于贝叶斯视觉变压器构建不确定性估计模型，实现精准主动学习选样。
- 在百万级未知恶意软件上实验，显著降低标注需求，性能优于传统SVM方法。
- 适用于各类未知恶意软件检测任务，尤其适合标注成本高的安全场景。
主动学习通过选择当前模型最不确定的未标记样本交由专家标注，以降低标注成本。本文提出两种新型主动学习方法，用于标注属于不同未知现代恶意软件家族的一百万个恶意软件样本。第一种方法结合Inception-V4+PCA与多种支持向量机（UTSVM、PSVM、SVM-GSU、TBSVM）；第二种方法为基于视觉变换器的贝叶斯神经网络（ViT-BNN）。所提出的ViT-BNN是一种先进的主动学习框架，区别于现有方法，可泛化至任意任务。实验表明，该模型在处理不确定性方面更具稳定性与鲁棒性。
原文摘要 · Abstract (English)
Active learning for classification seeks to reduce the cost of labeling samples by finding unlabeled examples about which the current model is least certain and sending them to an annotator/expert to label. Bayesian theory can provide a probabilistic view of deep neural network models by asserting a prior distribution over model parameters and estimating the uncertainties by posterior distribution over these parameters. This paper proposes two novel active learning approaches to label one million malware examples belonging to different unknown modern malware families. The first model is Inception-V4+PCA combined with several support vector machine (SVM) algorithms (UTSVM, PSVM, SVM-GSU, TBSVM). The second model is Vision Transformer based Bayesian Neural Networks ViT-BNN. Our proposed ViT-BNN is a state-of-the-art active learning approach that differs from current methods and can apply to any particular task. The experiments demonstrate that the ViT-BNN is more stable and robust in handling uncertainty.
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