穿透静态：通过可解释性揭开恶意软件可视化的神秘面纱
Through the Static: Demystifying Malware Visualization via Explainability
AI总结：
本研究复现六个CNN模型并利用CAM技术评估其在MalImg、Big2015和VX-Zoo数据集上的可解释性，同时利用提取的热力图增强视觉Transformer分类器性能，使F1分数提升2%至8%。
摘要：
安全研究人员正努力应对恶意文件的激增，这需要快速识别和分类恶意软件家族以实现有效防护。视觉分类器，尤其是卷积神经网络（CNN），已成为完成该任务的重要工具。然而，在医学和自动驾驶等其他高风险领域中常见的鲁棒性和可解释性问题，在当前文献中仍未得到充分研究。尽管研究中提出的深度学习可视化分类器在无需专家特征提取的情况下取得了优异结果，但尚未对其可复现性进行适当研究。此外，文献对于此类分类器如何得出其答案尚不清晰。我们的研究通过复现六个CNN模型并探究其缺陷来填补这些空白。我们采用类激活映射（CAM），如GradCAM和HiResCAM，来评估模型的可解释性。我们在两个标准数据集MalImg和Big2015，以及一个名为VX-Zoo的新建数据集上评估CNN的性能和可解释性。我们运用这些不同的CAM技术来衡量每个模型的可解释性。借助这些工具，我们调查了导致不同模型对输入产生不同解释的潜在因素，使人类研究人员能够识别出对区分不同恶意软件家族至关重要的模式，并解释CNN模型得出结论的原因。除了强调可解释性研究中发现的模式外，我们还利用提取的热力图来增强视觉Transformer分类器的性能和解释质量。与基准值相比，该方法在所有数据集上的F1分数获得了2%到8%的显著提升。
英文摘要：
Security researchers grapple with the surge of malicious files, necessitating swift identification and classification of malware strains for effective protection. Visual classifiers and in particular Convolutional Neural Networks (CNNs) have emerged as vital tools for this task. However, issues of robustness and explainability, common in other high risk domain like medicine and autonomous vehicles, remain understudied in current literature. Although deep learning visualization classifiers presented in research obtain great results without the need for expert feature extraction, they have not been properly studied in terms of their replicability. Additionally, the literature is not clear on how these types of classifiers arrive to their answers. Our study addresses these gaps by replicating six CNN models and exploring their pitfalls. We employ Class Activation Maps (CAMs), like GradCAM and HiResCAM, to assess model explainability. We evaluate the CNNs' performance and interpretability on two standard datasets, MalImg and Big2015, and a newly created called VX-Zoo. We employ these different CAM techniques to gauge the explainability of each of the models. With these tools, we investigate the underlying factors contributing to different interpretations of inputs across the different models, empowering human researchers to discern patterns crucial for identifying distinct malware families and explain why CNN models arrive at their conclusions. Other then highlighting the patterns found in the interpretability study, we employ the extracted heatmpas to enhance Visual Transformers classifiers' performance and explanation quality. This approach yields substantial improvements in F1 score, ranging from 2% to 8%, across the datasets compared to benchmark values.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
