Artificial Intelligence-Based Web Attack Detection: A Survey of Machine Learning, Deep Learning, Transformer and Large Language Model Approaches

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Web services are now pervasive in almost all industries, but this has made them a major target for hackers. Traditional signature-based solutions are no longer effective against zero‑day and polymorphic threats that evolve rapidly. The much heralded game‑changer is artificial intelligence (AI) particularly deep learning and more recently large language models. But the literature is fragmented: CNNs learn local features, Transformers capture long-range dependencies, and LLMs offer semantic understanding - yet each suffers from real-world limitations on interpretability, efficiency and security. In this survey we chart this progress and suggest that it's not model accuracy but trustworthiness that matters most. We carve up benchmark datasets (CSIC2010, CICIDS2017) and see a disconnect between lab and reality. Our message: the next big jump will not be driven by larger parameters, but smaller, more visible and dynamic systems that can be trusted by security analysts.