Hybrid AI-Driven Secure Techniques for Real-Time Mitigation of Cybersecurity Threats
W. Sarada, K. Shanthi, Krishna Priya, Omda Kumar, Senthilkumar P, T J Nandhini · 2025
The rapid evolution of cyber threats necessitates the development of advanced security mechanisms capable of real-time detection and mitigation. A comprehensive study examines how AI-based security combinations improve defenses to combat present and future limiting threats. The proposed framework combines machine learning (ML) with deep learning (DL) and rule-based expert systems which provides an adaptable and proactive protection system. The integrated security solution uses anomalous behavior detection systems together with predefined threat signatures together with automated response mechanisms to counter malware and phishing and distributed denial-of-service attacks. Through reinforcement learning applications the system gains heightened adaptivity to changing threat profiles and achieved better predicting results. The research adds blockchain-based authentication frameworks to secure data handling and provides enhanced protection by ensuring both data integrity and confidentiality. System vulnerabilities decrease simultaneously with real-time threat intelligence which operates through AI-powered automation to decrease response times. Performance analysis of the proposed model on real cybersecurity data demonstrates superior handling of accuracy, efficiency and robustness when compared with typical methods. Research demonstrates how combination frameworks that involve AI control the capability for sophisticated threat reduction while shaping cyber defense systems of the future. These outcomes support the progress of security systems which integrate intelligence with scalability and resilience for protecting critical facilities alongside enterprise networks.