AI-Driven Cyber Defense: Applications of Machine Learning, NLP, and Reinforcement Approaches to Threat Detection

Abstract

This research publication examines AI-powered phishing detection systems, the interventions of Explainable AI (XAI) in cybersecurity using large language models, the applications of deepfake forensics in detecting manipulated media through machine learning, the use of BERT and GPT models in identifying malicious intent in text-based attacks, and the implications of Deep Reinforcement Learning for intrusion detection. Drawing on empirical research across each of these domains, the paper evaluates the accuracy, scalability, and real-world applicability of current AI-driven approaches to cyber defense, while addressing the ethical, legal, and societal considerations that accompany their deployment.