论文标题
通过无监督的签名生成来支持软件供应链攻击的检测
Supporting the Detection of Software Supply Chain Attacks through Unsupervised Signature Generation
论文作者
论文摘要
软件供应链攻击中使用的木马化软件包构成了新兴威胁。不幸的是，仍然缺乏可扩展的方法，这些方法允许对恶意软件包进行自动和及时检测，因此大多数检测是基于手动劳动和专业知识。但是，已经观察到，大多数攻击活动都包含共享相同或相似恶意代码的多个软件包。我们利用这一事实自动复制在现实世界攻击中使用的已知恶意软件包的簇，从而减少了对专家知识和手动检查的需求。我们的方法是使用MCL来模仿专业知识的AST聚类（ACME），得出有希望的结果，$ f_ {1} $得分为0.99。签名是根据簇的特征代码片段自动生成的，随后被用于扫描整个NPM注册表中未报告的恶意软件包。我们能够识别并报告六个恶意软件包，这些套件已从NPM删除。因此，我们的方法可以通过减少体力劳动来支持分析师，因此可以及时检测可能的软件供应链攻击。
Trojanized software packages used in software supply chain attacks constitute an emerging threat. Unfortunately, there is still a lack of scalable approaches that allow automated and timely detection of malicious software packages and thus most detections are based on manual labor and expertise. However, it has been observed that most attack campaigns comprise multiple packages that share the same or similar malicious code. We leverage that fact to automatically reproduce manually identified clusters of known malicious packages that have been used in real world attacks, thus, reducing the need for expert knowledge and manual inspection. Our approach, AST Clustering using MCL to mimic Expertise (ACME), yields promising results with a $F_{1}$ score of 0.99. Signatures are automatically generated based on characteristic code fragments from clusters and are subsequently used to scan the whole npm registry for unreported malicious packages. We are able to identify and report six malicious packages that have been removed from npm consequentially. Therefore, our approach can support analysts by reducing manual labor and hence may be employed to timely detect possible software supply chain attacks.
