用测试时自监督训练，让杀毒模型自动适应新病毒
MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
- 测试时用少量新样本自监督微调编码器
- 在持续检测中准确率优于传统方法
- 适合需要长期更新的安卓恶意软件防护
我们提出MADCAT，一种针对恶意软件检测中概念漂移问题的自监督方法。MADCAT采用编码器-解码器架构，在测试时通过对少量平衡的测试数据进行训练，利用自监督目标优化编码器。在测试时训练过程中，模型学习到对已见旧数据和新到来样本都有效的特征。我们在连续的Android恶意软件检测场景中验证了MADCAT的有效性，其在测试时检测性能始终优于基线方法。此外，我们还展示了MADCAT与已有方法结合能进一步提升应对概念漂移的能力。
原文摘要 · Abstract (English)
We present MADCAT, a self-supervised approach designed to address the concept drift problem in malware detection. MADCAT employs an encoder-decoder architecture and works by test-time training of the encoder on a small, balanced subset of the test-time data using a self-supervised objective. During test-time training, the model learns features that are useful for detecting both previously seen (old) data and newly arriving samples. We demonstrate the effectiveness of MADCAT in continuous Android malware detection settings. MADCAT consistently outperforms baseline methods in detection performance at test time. We also show the synergy between MADCAT and prior approaches in addressing concept drift in malware detection
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