Computer Science > Software Engineering
arXiv:2306.11673 (cs)
[Submitted on 20 Jun 2023]
Title:A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning
Authors:Nima Shiri Harzevili,Alvine Boaye Belle,Junjie Wang,Song Wang,Zhen Ming (Jack)Jiang,Nachiappan Nagappan
View a PDF of the paper titled A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning, by Nima Shiri Harzevili and 5 other authors
View PDFAbstract:Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures to be implemented before they may be exploited. Automatic vulnerability identification is important because it can evaluate large codebases more efficiently than manual code auditing. Many Machine Learning (ML) and Deep Learning (DL) based models for detecting vulnerabilities in source code have been presented in recent years. However, a survey that summarises, classifies, and analyses the application of ML/DL models for vulnerability detection is missing. It may be difficult to discover gaps in existing research and potential for future improvement without a comprehensive survey. This could result in essential areas of research being overlooked or under-represented, leading to a skewed understanding of the state of the art in vulnerability detection. This work address that gap by presenting a systematic survey to characterize various features of ML/DL-based source code level software vulnerability detection approaches via five primary research questions (RQs). Specifically, our RQ1 examines the trend of publications that leverage ML/DL for vulnerability detection, including the evolution of research and the distribution of publication venues. RQ2 describes vulnerability datasets used by existing ML/DL-based models, including their sources, types, and representations, as well as analyses of the embedding techniques used by these approaches. RQ3 explores the model architectures and design assumptions of ML/DL-based vulnerability detection approaches. RQ4 summarises the type and frequency of vulnerabilities that are covered by existing studies. Lastly, RQ5 presents a list of current challenges to be researched and an outline of a potential research roadmap that highlights crucial opportunities for future work.
| Subjects: | Software Engineering (cs.SE) |
| Cite as: | arXiv:2306.11673 [cs.SE] |
| (orarXiv:2306.11673v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2306.11673 arXiv-issued DOI via DataCite |
Submission history
From: Nima Shiri Harzevili [view email][v1] Tue, 20 Jun 2023 16:51:59 UTC (4,700 KB)
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning, by Nima Shiri Harzevili and 5 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.