Malicious source code detection using a translation model.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 37521045.
- Also identified by DOI 10.1016/j.patter.2023.100773 and PMC identifier 10382987.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Modern software development often relies on open-source code sharing. Open-source code reuse, however, allows hackers to access wide developer communities, thereby potentially affecting many products. An increasing number of such "supply chain attacks" have occurred in recent years, taking advantage of open-source software development practices. Here, we introduce the Malicious Source code Detection using a Translation model (MSDT) algorithm. MSDT is a novel deep-learning-based analysis method that detects real-world code injections into source code packages. We have tested MSDT by embedding examples from a dataset of over 600,000 different functions and then applying a clustering algorithm to the resulting embedding vectors to identify malicious functions by detecting outliers. We evaluated MSDT's performance with extensive experiments and demonstrated that MSDT could detect malicious code injections with <i>precision@k</i> values of up to 0.909.