Predicting Vulnerable Software Components through N-Gram Analysis and Statistical Feature Selection-Reference-Cited by-同舟云学术

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Predicting Vulnerable Software Components through N-Gram Analysis and Statistical Feature Selection

Published:2015-12 Issue: Volume: Page:
ISSN:
Container-title:2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)
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Author:

Pang Yulei,Xue Xiaozhen,Namin Akbar Siami

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/7420298/7424247/07424372.pdf?arnumber=7424372

Cited by 59 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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2. Towards a Block-Level ML-Based Python Vulnerability Detection Tool;Acta Cybernetica;2024-07-22

3. SyzRisk: A Change-Pattern-Based Continuous Kernel Regression Fuzzer;Proceedings of the 19th ACM Asia Conference on Computer and Communications Security;2024-07

4. VulDL: Tree-based and Graph-based Neural Networks for Vulnerability Detection and Localization;Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering;2024-06-18

5. GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning;Journal of Systems and Software;2024-06

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