Open Access
| Issue |
ITM Web Conf.
Volume 87, 2026
2nd International Conference on Computing Paradigms (ICCP-2026)
|
|
|---|---|---|
| Article Number | 01011 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/itmconf/20268701011 | |
| Published online | 30 June 2026 | |
- OWASP Foundation, “OWASP Top 10 Web Application Security Risks,” 2021. [Google Scholar]
- J. Fonseca and M. Vieira, “Web Application Security Testing: A Systematic Literature Review,” IEEE Access, vol. 5, pp. 10713–10735, 2017. [Google Scholar]
- H. Holm, “Performance of Automated exploit vector Scanning Tools,” in Proc. IEEE Int. Conf. on Availability, Reliability and Security, 2015. [Google Scholar]
- R. Sommer and V. Paxson, “Outside the Closed World: On Using predictive model for Network Intrusion identification,” in Proc. IEEE Symp. on Security and Privacy, 2010. [Google Scholar]
- Z. Li, J. Zhang, and X. Wang, “Deep Learning-Based exploit vector identification: A Survey,” IEEE Access, vol. 9, pp. 14396–14415, 2021. [Google Scholar]
- J. Chen, X. Zhang, and Y. Li, “predictive model for Zero-Day exploit vector identification,” Computers & Security, vol. 93, 2020. [Google Scholar]
- W. Xiong, R. Lagutin, and J. He, “Cybersecurity Risk Assessment Using predictive model,” Future Generation Computer Systems, vol. 98, pp. 208–219, 2019. [Google Scholar]
- M. Almorsy, J. Grundy, and I. Mu¨ller, “An Analysis of the Cloud Computing Security Problem,” in Proc. Asia-Pacific Software Engineering Conf., 2010. [Google Scholar]
- S. Behl, Cybersecurity and Cyberwar: What Everyone Needs to Know, Oxford University Press, 2017. [Google Scholar]
- M. Kempanna, P. Pushpa, and M. Rangaswamy, “BERT Model Based Identification and Classification of Web Vulnerabilities Using Deep Learning,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 21s, pp. 236–248, 2024. [Google Scholar]
- A. Sharma, R. Gupta, and S. Verma, “Defect-Scanner: A Comparative Study of Language Models for Software exploit vector identification,” International Journal of Information Security, vol. 23, pp. 3513–3526, 2024. [Google Scholar]
- A. Alshammari, M. Khan, and R. Kumar, “AI-Driven Anomaly identification system for Cybersecurity in Smart Systems,” Electronics, vol. 14, no. 12, 2025. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.