mlsanityData to 14 Oct 2026

A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks

arXiv 2403.10646 ↗First posted 15 Mar 2024cs.LG cs.CRcited by 5

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Abstract (arXiv)

Machine learning techniques for cybersecurity-related software engineering tasks are becoming increasingly popular. The representation of source code is a key portion of the technique that can impact the way the model is able to learn the features of the source code. With an increasing number of these techniques being developed, it is valuable to see the current state of the field to better understand what exists and what is not there yet. This article presents a study of these existing machine learning based approaches and demonstrates what type of representations were used for different cybersecurity tasks and programming languages. Additionally, we study what types of models are used with different representations. We have found that graph-based representations are the most popular category of representation, and tokenizers and Abstract Syntax Trees (ASTs) are the two most popular representations overall (e.g., AST and tokenizers are the representations with the highest count of papers, whereas graph-based representations is the category with the highest count of papers). We also found that the most popular cybersecurity task is vulnerability detection, and the language that is covered by the most techniques is C. Finally, we found that sequence-based models are the most popular category of models, and Support Vector Machines are the most popular model overall.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 60 of its 213 references that are in the map. First posted on arXiv 15 Mar 2024.

Builds on

60 of 213 references.

References · 60

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38 more
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50 most cited of 60 All 60 (JSON)

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Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2025Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Predictioncited by 5
  2. 2025MLCPD: A Unified Multi-Language Code Parsing Dataset with Universal AST Schemacited by 2
  3. 2024A Systematic Literature Review on the Use of Machine Learning in Software Engineeringcited by 0
  4. 2025Exposing and Defending Membership Leakage in Vulnerability Prediction Modelscited by 0
  5. 2025From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detectioncited by 0