Artificial Intelligence in Cybersecurity: Machine Learning and Deep Learning for Intelligent Threat Detection

The rapid expansion of digital infrastructure has increased exposure to complex cyber threats that traditional rule based security systems struggle to detect. This paper examines how artificial intelligence, particularly machine learning and deep learning, strengthens intelligent threat detection through adaptive intrusion detection systems, anomaly identification, and predictive threat intelligence. It reviews recent developments showing how deep learning models extract features from high dimensional network traffic and detect subtle behavioral deviations. At the same time, it addresses ongoing concerns related to adversarial manipulation, data imbalance, and model interpretability. The study provides a structured analysis of AI driven cybersecurity frameworks and outlines emerging directions for building more resilient and transparent defense systems.