101 list 5906441 bytes 2025 Stevanović, Nikola M., 1992 srp http://creativecommons.org/licenses/by-nc-nd/3.0/at/legalcode Veštačke neuronske mreže za detekciju veb napada info:eu-repo/semantics/bachelorThesis This dissertation presents a comprehensive approach to web attackdetection using artificial neural networks. The collection of diversemalicious web traffic was carried out using honeypots, enabling thecreation of a robust dataset for training models to identify cyberthreats. The study addresses zero-day attack detection through variousmachine learning techniques capable of identifying previouslyunknown vulnerabilities in network traffic. To reduce catastrophicforgetting in dynamic attack environments, the use of multipleincremental learning strategies has been proposed, which enablecontinuous model adaptation with minimal loss of previouslyacquired knowledge. Тhe study introduces a population-based featureselection method, which improves classification efficiency byfocusing on the most relevant network features. The dissertationpresents a deep learning model for phishing email detection, based onthe architectures of recurrent and convolutional neural networks.Moreover, advanced feature weighting and embedding techniques areemployed to enhance phishing website detection. By integrating thesemethods, this dissertation provides a scalable and adaptive solutionfor real-time detection of web-based threats, offering significantadvancements in the fields of web security and machine learning. Biografija autora: str. [103].Bibliografija: str. [92-102]. Datum odbrane: 15.07.2025. Artificial intelligence https://phaidrani.ni.ac.rs/o:3009 cobiss:173068041 thesis:8783 Todorović, Branimir, 1967- Ćirić, Miroslav, 1964- Miladinović, Marko, 1979- Trokicić, Aleksandar B., 1989- Janković, Dragan, 1967-