Profile of Vulnerability Remediations in Dependencies Using Graph Analysis (2403.04989v1)
Abstract: This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) to the critical challenge of open source package vulnerability remediation by analyzing control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Our approach uniquely applies node centrality metrics -- degree, norm, and closeness centrality -- to the GAT model, enabling a detailed examination of package code interactions with a focus on identifying and understanding vulnerable nodes, and when dependency package upgrades will interfere with application workflow. The study's application on a varied dataset reveals an unexpected limited inter-connectivity of vulnerabilities in core code, thus challenging established notions in software security. The results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities, proving its potential in advancing cybersecurity measures. This approach not only aids in the strategic mitigation of vulnerabilities but also lays the groundwork for the development of sophisticated, sustainable monitoring systems for the evaluation of work effort for vulnerability remediation resulting from open source software. The insights gained from this study mark a significant advancement in the field of package vulnerability analysis and cybersecurity.
- Scada vulnerabilities and attacks: A review of the state-of-the-art and open issues. Computers & Security, page 103028, 2022.
- Empirical analysis of security vulnerabilities in python packages. Empirical Software Engineering, 28(3):59, 2023.
- Automated risk management based software security vulnerabilities management. IEEE Access, 10:90597–90608, 2022.
- Detecting critical nodes in sparse graphs. Computers & Operations Research, 36(7):2193–2200, 2009.
- Parallel algorithms for evaluating centrality indices in real-world networks. In 2006 International Conference on Parallel Processing (ICPP’06), pages 539–550, 2006.
- Cyclomatic complexity. IEEE software, 33(6):27–29, 2016.
- A c/c++ code vulnerability dataset with code changes and cve summaries. In Proceedings of the 17th International Conference on Mining Software Repositories, MSR ’20, page 508–512, New York, NY, USA, 2020. Association for Computing Machinery.
- Görkem Giray. A software engineering perspective on engineering machine learning systems: State of the art and challenges. Journal of Systems and Software, 180:111031, 2021.
- On the capability of static code analysis to detect security vulnerabilities. Information and Software Technology, 68:18–33, 2015.
- The connected-component labeling problem: A review of state-of-the-art algorithms. Pattern Recognition, 70:25–43, 2017.
- Knowledge graphs. ACM Computing Surveys, 54(4):1–37, jul 2021.
- A comparative study of vulnerability reporting by software composition analysis tools. In Proceedings of the 15th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), pages 1–11, 2021.
- A practical approach to constructing a knowledge graph for cybersecurity. Engineering, 4(1):53–60, 2018.
- Recent progress of using knowledge graph for cybersecurity. Electronics, 11(15):2287, 2022.
- A review of knowledge graph application scenarios in cyber security. arXiv preprint arXiv:2204.04769, 2022.
- Thomas J McCabe. A complexity measure. IEEE Transactions on software Engineering, (4):308–320, 1976.
- Mark EJ Newman. Mixing patterns in networks. Physical review E, 67(2):026126, 2003.
- UJ Nieminen. On the centrality in a directed graph. Social science research, 2(4):371–378, 1973.
- Estimating node importance in knowledge graphs using graph neural networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 596–606, 2019.
- Edward L Platt. Network science with Python and NetworkX quick start guide: explore and visualize network data effectively. Packt Publishing Ltd, 2019.
- Arachne: An arkouda package for large-scale graph analytics. In 2022 IEEE High Performance Extreme Computing Conference (HPEC), pages 1–7. IEEE, 2022.
- Generalizations of the clustering coefficient to weighted complex networks. Physical Review E, 75(2):027105, 2007.
- Cyclomatic complexity: The nesting problem. In Eighth International Conference on Digital Information Management (ICDIM 2013), pages 274–279. IEEE, 2013.
- Robert Seacord. Secure coding to prevent vulnerabilities. Carnegie Mellon University, Software Engineering Institute’s Insights (blog), May 2014. Accessed: 2023-Nov-16.
- Leslie F Sikos. Cybersecurity knowledge graphs. Knowledge and Information Systems, pages 1–21, 2023.
- Automated vulnerability analysis: Leveraging control flow for evolutionary input crafting. In Twenty-Third Annual Computer Security Applications Conference (ACSAC 2007), pages 477–486. IEEE, 2007.
- Tamás Szabó. Incrementalizing production codeql analyses. arXiv preprint arXiv:2308.09660, 2023.
- Feature models to boost the vulnerability management process. Journal of Systems and Software, 195:111541, 2023.
- Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
- An empirical study on software bill of materials: Where we stand and the road ahead. arXiv preprint arXiv:2301.05362, 2023.
- Coupled attention networks for multivariate time series anomaly detection. IEEE Transactions on Emerging Topics in Computing, 2023.
- A review on application of knowledge graph in cybersecurity. In 2020 International Signal Processing, Communications and Engineering Management Conference (ISPCEM), pages 240–243. IEEE, 2020.
- Constructing more complete control flow graphs utilizing directed gray-box fuzzing. Applied Sciences, 11(3):1351, 2021.
- Afra Zomorodian. Computational topology. Algorithms and theory of computation handbook, 2(3), 2009.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Collections
Sign up for free to add this paper to one or more collections.
