A Systematic Review on the Evaluation of Advanced Approaches in Cyberattack Detection (#368)

Authors

Gonzales Barcayola, Jordan Piero

Gomez Lizama, Juan Jose

Cuevas Tenorio, Luis Enrique

Abstract

This article presents a comprehensive study on the detection of cyberattacks on websites, highlighting the increase in threats such as phishing, SQL injection and DDoS attacks, which compromise data security and user trust. Through a methodology aimed at the review and collection of 21 recent studies, methods based on artificial intelligence were evaluated, such as neural networks and behavioral analysis, which surpass traditional approaches, such as firewalls and IDS, in precision and adaptability. The results underline that the most effective strategies are based on adaptive approaches and emerging technologies, integrating dynamic systems capable of responding to contextual changes and advanced analytical tools to mitigate risks. This review contributes to consolidating an updated and detailed overview, identifying knowledge gaps and establishing a solid foundation for the development of innovative and robust solutions that strengthen security in modern web applications.