Supporting the Detection of Software Supply Chain Attacks through Unsupervised Signature Generation
This work addresses the need for scalable, automated detection of software supply chain attacks to reduce reliance on manual expertise, though it is incremental in building on existing clustering techniques.
The paper tackles the problem of detecting malicious software packages in supply chain attacks by automatically generating signatures from clusters of known malicious packages, achieving an F1 score of 0.99 and identifying six previously unreported malicious packages on the npm registry.
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.