References
- Acunetix. (2020). Web Application Vulnerability Report 2020. Retrieved January 10, 2020, from https://www.acunetix.com/wp-content/uploads/2020/10/Acunetix_2020_Web_Application_Vulnerability_Report.pdf.
- Al Anhar, A., & Suryanto, Y. (2021). Evaluation of web application vulnerability scanner for modern web application. In 2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), pp. 200–204. https://doi.org/10.1109/ICAICST53116.2021.9497831
- Calzavara, S., Conti, M., Focardi, R., Rabitti, A., & Tolomei, G. (2020). Machine learning for web vulnerability detection: The case of cross-site request forgery. IEEE Security & Privacy, 18(3), 8–16. https://doi.org/10.1109/MSEC.2019.2961649
- Del Verme, M., Sommervoll, Å. Å., Erdődi, L., Totaro, S., & Zennaro, F. M. (2021). Sql injections and reinforcement learning: An empirical evaluation of the role of action structure. In N. Tuveri, A. Michalas, and B. B. Brumley (Eds.), Secure IT systems (pp. 95–113). Springer International Publishing.
- Elder, S. (2021). Vulnerability detection is just the beginning. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (pp. 304–308). https://doi.org/10.1109/ICSE-Companion52605.2021.00133
- Gartner. (2021). Burp suite professional reviews. Retrieved December 28, 2021, from https://www.gartner.com/reviews/market/application-security-testing/vendor/portswigger/product/burp-suite-professional.
- Google Trends. (2021). Google trends and compare. Retrieved December 12, 2021, from https://trends.google.com/trends/explore?date=2015-01-01_2020-12-30&q=vulnerability, cyber_security,machine_learning
- Holik, F., & Neradova, S. (2017). Vulnerabilities of modern web applications. In 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 1256–1261). https://doi.org/10.23919/MIPRO.2017.7973616
- Internet World Stats. (2021). World internet usage and population statistics. Retrieved December 12, 2021, from https://www.internetworldstats.com/stats.htm.
- Jan, S., Panichella, A., Arcuri, A., & Briand, L. (2019). Automatic generation of tests to exploit xml injection vulnerabilities in web applications. IEEE Transactions on Software Engineering, 45(4), 335–362. https://doi.org/10.1109/TSE.2017.2778711
- Johari, R., & Sharma, P. (2012). A survey on web application vulnerabilities (sqlia, xss) exploitation and security engine for sql injection. In 2012 International Conference on Communication Systems and Network Technologies (pp. 453–458).
- Kaspersky. (2021). 2021 Top Ten Cybersecurity Trends. Retrieved December 12, 2021, from https://www.kaspersky.com/resource-center/preemptive-safety/cyber-security-trends.
- López de Jiménez, R. E. (2016). Pentesting on web applications using ethical – Hacking. In 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI) (pp. 1–6).
- Olson, R. S., La Cava, W., Mustahsan, Z., Varik, A., & Moore, J. H. (2018). Data-driven advice for applying machine learning to bioinformatics problems. In Pacific symposium on biocomputing (Vol. 212669, pp. 192–203). World Scientific Publishing Co. Pte Ltd. https://doi.org/10.1142/9789813235533_0018
- Omeiza, D., & Owusu-Tweneboah, J. (2018). Web security investigation through penetration tests: A case study of an educational institution portal. CoRR Abs/1811, 01388, 1–4.
- OWASP Top 10. (2021). Welcome to the OWASP Top 10 – 2021. Retrieved December 28, 2021, from https://owasp.org/Top10/.
- Sai Kiran, K., Devisetty, R. K., Kalyan, N. P., Mukundini, K., & Karthi, R. (2020). Building a intrusion detection system for Iot environment using machine learning techniques. Procedia Computer Science, 171, 2372–2379. https://doi.org/10.1016/j.procs.2020.04.257
- TCELL by Rapid7. (2020). Security report for in-production web applications. Retrieved from Retrieved January 10, 2022, from https://www.rapid7.com/globalassets/_pdfs/whitepaperguide/rapid7-tcell-application-security-report.pdf.
- The European Union Agency for Cybersecurity – ENISA. (2020). AI cybersecurity challenges. Retrieved November 29, 2021, from https://www.enisa.europa.eu/publications/artificial-intelligence-cybersecurity-challenges/@@download/fullReport.