La inteligencia artificial en la detección de intrusos en infraestructura de TI. - Herrera Flores, Anthony Aron (2025)
Digitalization has turned IT infrastructures into targets of increasingly sophisticated cyber attacks, highlighting the limitations of traditional intrusion detection methods. In this context, Artificial Intelligence (AI) emerges as a key tool to improve threat identification, reduce false positives and automate incident response. This study analyzes the impact of AI in cybersecurity, comparing algorithms such as Random Forest, Neural Networks and Support Vector Machines (SVM) in intrusion detection. In addition, critical factors for its implementation are identified, such as data quality, costs and staff training. The findings highlight that AI significantly improves threat detection and adaptation to new attacks. However, its adoption faces technical and economic challenges. Finally, strategies are proposed to optimize its integration into business environments, guaranteeing more efficient protection against cyber attacks.