e-ISSN 2231-8542
ISSN 1511-3701
J
Pertanika Journal of Tropical Agricultural Science, Volume J, Issue J, January J
Keywords: J
Published on: J
J
Abaimov, S., & Bianchi, G. (2019). CODDLE: Code-injection detection with deep learning. IEEE Access, 7, 128617-128627. https://doi.org/10.1109/ACCESS.2019.2939870
Bates, D., Barth, A., & Jackson, C. (2010). Regular expressions considered harmful in client-side XSS filters. In Proceedings of the 19th International Conference on World Wide Web (pp. 91-100). ACM Publishing. https://doi.org/10.1145/1772690.1772701
Cui, Y., Cui, J., & Hu, J. (2020). A survey on XSS attack detection and prevention in web applications. In Proceedings of the 2020 12th International Conference on Machine Learning and Computing (pp. 443-449). ACM Publishing. https://doi.org/10.1145/3383972.3384027
Gan, J. M., Ling, H. Y., & Leau, Y. B. (2020). A Review on detection of cross-site scripting attacks (XSS) in web security. In M. Anbar, N. Abdullah, & S. Manickam (Eds.), International Conference on Advances in Cyber Security (Vol. 1347, pp. 685-709). Springer. https://doi.org/10.1007/978-981-33-6835-4_45
Giménez, C. T., Villegas, A. P., & Marañón, G. Á. (2010). HTTP data set CSIC 2010. Information Security Institute of CSIC (Spanish Research National Council). https://www.tic.itefi.csic.es/dataset/
Jabiyev, B., Sprecher, S., Onarlioglu, K., & Kirda, E. (2021). T-Reqs: HTTP request smuggling with differential fuzzing. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (pp. 1805-1820). ACM Publishing. https://doi.org/10.1145/3460120.3485384
Khazal, I. F., & Hussain, M. A. (2021). Server side method to detect and prevent stored XSS attack. Iraqi Journal for Electrical & Electronic Engineering, 17(2), 58-65. https://doi.org/10.37917/ijeee.17.2.8
Liu, M., Zhang, B., Chen, W., & Zhang, X. (2019). A survey of exploitation and detection methods of XSS vulnerabilities. IEEE Access, 7, 182004-182016. https://doi.org/10.1109/ACCESS.2019.2960449
Rodríguez, G. E., Torres, J. G., Flores, P., & Benavides, D. E. (2020). Cross-site scripting (XSS) attacks and mitigation: A survey. Computer Networks, 166, Article 106960. https://doi.org/10.1016/j.comnet.2019.106960
Swiat. (2008). IE 8 XSS filter architecture/implementation. Microsoft. https://msrc.microsoft.com/blog/2008/08/ie-8-xss-filter-architecture-implementation/
Sarmah, U., Bhattacharyya, D. K., & Kalita, J. K. (2018). A survey of detection methods for XSS attacks. Journal of Network and Computer Applications, 118, 113-143. https://doi.org/10.1016/j.jnca.2018.06.004
Satish, P. S., & Chavan, R. K. (2017). Web browser security: Different attacks detection and prevention techniques. International Journal of Computer Applications, 170(9), 35-41.
Shar, L. K., & Tan, H. B. K. (2011). Defending against cross-site scripting attacks. Computer, 45(3), 55-62. https://doi.org/10.1109/MC.2011.261
Stock, B., Lekies, S., Mueller, T., Spiegel, P., & Johns, M. (2014). Precise client-side protection against DOM-based cross-site scripting. In 23rd USENIX Security Symposium (pp. 655-670). USENIX Association.
Takahashi, H., Yasunaga, K., Mambo, M., Kim, K., & Youm, H. Y. (2013). Preventing abuse of cookies stolen by XSS. In 2013 Eighth Asia Joint Conference on Information Security (pp. 85-89). IEEE Publishing. https://doi.ieeecomputersociety.org/10.1109/ASIAJCIS.2013.20
Vartouni, A. M., Kashi, S. S., & Teshnehlab, M. (2018). An anomaly detection method to detect web attacks using stacked auto-encoder. In 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) (pp. 131-134). IEEE Publishing. https://doi.org/10.1109/CFIS.2018.8336654
Wichers, D., & Williams, J. (2017). OWASP top 10 - 2017. OWASP Foundation. https://owasp.org/www-pdf-archive/OWASP_Top_10-2017_%28en%29.pdf.pdf
The Chromium Projects. (2019). XXX Auditor. https://www.chromium.org/developers/design-documents/xss-auditor
Yavanoglu, O., & Aydos, M. (2017). A review on cyber security datasets for machine learning algorithms. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 2186-2193). IEEE Publishing. https://doi.org/10.1109/BigData.2017.8258167
ISSN 1511-3701
e-ISSN 2231-8542