Daily Plan

更新: 5/3/2025 字数: 0 字 时长: 0 分钟

#todo

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Daily Study

更新: 5/3/2025 字数: 0 字 时长: 0 分钟

vulnerability discovery

Zou, Quanchen, et al. "From automation to intelligence: Survey of research on vulnerability discovery techniques." Journal of Tsinghua University (Science and Technology) 58.12 (2018): 1079-1094.

Grieco, Gustavo, et al. "Toward large-scale vulnerability discovery using machine learning." Proceedings of the sixth ACM conference on data and application security and privacy. 2016.

Heelan, Sean. "Vulnerability detection systems: Think cyborg, not robot." IEEE Security & Privacy 9.3 (2011): 74-77.

Sean Heelan. 2011. Vulnerability Detection Systems: Think Cyborg, Not Robot. IEEE Security and Privacy 9, 3 (May 2011), 74–77. https://doi.org/10.1109/MSP.2011.70

Seyed Mohammad Ghaffarian and Hamid Reza Shahriari. 2017. Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey. ACM Comput. Surv. 50, 4, Article 56 (July 2018), 36 pages. https://doi.org/10.1145/3092566

P. C. van Oorschot, "Security as an Artificial Science, System Administration, and Tools," in IEEE Security & Privacy, vol. 20, no. 6, pp. 74-78, Nov.-Dec. 2022, doi: 10.1109/MSEC.2022.3197066. keywords: {Security;Software tools;Performance evaluation},

Vanegue, Julien and Sean Heelan. “SMT Solvers in Software Security.” Workshop on Offensive Technologies (2012).

邹权臣, 张涛, 吴润浦, 马金鑫, 李美聪, 陈晨, 侯长玉. 从自动化到智能化:软件漏洞挖掘技术进展. 清华大学学报(自然科学版), 2018, 58(12): 1079-1094.

Kodete, Chandra Shikhi, Bharadwaj Thuraka, Vikram Pasupuleti, and Saiteja Malisetty. 2024. “Determining the Efficacy of Machine Learning Strategies in Quelling Cyber Security Threats: Evidence from Selected Literatures”. Asian Journal of Research in Computer Science 17 (8):24-33. https://doi.org/10.9734/ajrcos/2024/v17i7487.

Bagheri A, Hegedűs P. Towards a Block-Level Conformer-Based Python Vulnerability Detection. Software. 2024; 3(3):310-327. https://doi.org/10.3390/software3030016

Mithun Acharya, Tao Xie, Jian Pei, and Jun Xu. 2007. Mining API patterns as partial orders from source code: From usage scenarios to specifications. In Proceedings of the the 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on Foundations of Software Engineering (ESEC/FSE’07). ACM, 25--34.

Daily Problem

更新: 5/3/2025 字数: 0 字 时长: 0 分钟