AI-Driven Cybersecurity: Building Adaptive Threat Detection Systems Using Deep Learning

Abstract

This paper explores the development of adaptive threat detection systems in cybersecurity by leveraging deep learning techniques. It investigates the integration of AI-driven models capable of dynamically identifying and responding to evolving cyber threats with enhanced accuracy and speed. Emphasizing the challenges posed by complex, rapidly changing attack patterns, this study evaluates advanced neural architectures and model interpretability to build robust, real-time detection frameworks. The findings demonstrate significant potential for deep learning to transform cybersecurity defenses through continuous adaptation and intelligent threat assessment.