MalCL：利用基于GAN的生成式重放对抗恶意软件分类中的灾难性遗忘
MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification
AI总结：
本文提出MalCL系统，利用带特征匹配损失的GAN生成高质量重放样本，并结合基于隐藏表示的样本选择方案，在Windows和Android恶意软件分类中显著缓解灾难性遗忘，平均准确率较其他GR模型提升28%。
摘要：
面向恶意软件分类的持续学习（CL）旨在应对恶意软件威胁快速演化和新类型频繁出现的问题。基于生成式重放（GR）的持续学习系统利用生成模型产生过去数据的合成版本，再将其与新数据结合以重新训练主模型。该领域的传统机器学习技术往往难以应对灾难性遗忘，即模型在旧数据上的性能随时间推移而下降。本文提出一种基于GR的持续学习系统，该系统采用具有特征匹配损失的生成对抗网络（GAN）来生成高质量的恶意软件样本。此外，我们基于模型的隐藏表示实现了创新的重放样本选择方案。在类增量学习场景下——即新类别在多个任务中不断被引入——我们在Windows和Android恶意软件数据集上进行了全面评估，结果表明与先前方法相比性能有显著提升。例如，我们的系统在Windows恶意软件样本上实现了55%的平均准确率，显著优于其他基于GR的模型28%。本研究为推进基于GR的恶意软件分类系统提供了实用见解。实现代码可在https://github.com/MalwareReplayGAN/MalCL获取（代码将在论文展示后公开）。
英文摘要：
Continual Learning (CL) for malware classification tackles the rapidly evolving nature of malware threats and the frequent emergence of new types. Generative Replay (GR)-based CL systems utilize a generative model to produce synthetic versions of past data, which are then combined with new data to retrain the primary model. Traditional machine learning techniques in this domain often struggle with catastrophic forgetting, where a model's performance on old data degrades over time. In this paper, we introduce a GR-based CL system that employs Generative Adversarial Networks (GANs) with feature matching loss to generate high-quality malware samples. Additionally, we implement innovative selection schemes for replay samples based on the model's hidden representations. Our comprehensive evaluation across Windows and Android malware datasets in a class-incremental learning scenario -- where new classes are introduced continuously over multiple tasks -- demonstrates substantial performance improvements over previous methods. For example, our system achieves an average accuracy of 55% on Windows malware samples, significantly outperforming other GR-based models by 28%. This study provides practical insights for advancing GR-based malware classification systems. The implementation is available at \url {https://github.com/MalwareReplayGAN/MalCL}\footnote{The code will be made public upon the presentation of the paper}.
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。
