Damiano Ferla
Enhancing Cloud Based Web Application Firewall with Machine Learning models for Bot Detection and HTTP Traffic Classification.
Rel. Cataldo Basile. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract
Recently, cybersecurity attacks has become increasingly complex, with an increase in automated attacks and vulnerabilities exploitations in web applications. Online threats, such as bots or Cross-Site Scripting attacks, represent new challenges for data or user protection. According to the Imperva 2023 report, 49.6% of Internet traffic is composed of bots. Of these, 32% are bad bots, that perform automated tasks with malicious intent, such as extracting data from websites without permission to reuse them and gain a competitive advantage. Cross-Site Scripting and Injection in general, are firmly planted in the OWASP annual report. In the 2023 report, the category dedicated to Injection is in third place.
Improvements in machine learning, particularly through unsupervised learning techniques, have opened up new solutions for the detection and prevention of these cyber threats
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