Deep Learning Driven Big Data Architecture for Scalable Intelligent Network Threat Detection

This study proposes a deep learning driven big data architecture designed to enable scalable and intelligent network threat detection in high volume traffic environments. Increasing network traffic volume and heterogeneity generated by enterprise systems, cloud services, and Internet of Things devices require more adaptive and intelligent security mechanisms beyond traditional signature-based approaches. This study aims to develop an intelligent threat-detection framework that leverages deep-learning models and big data analytics to enhance detection accuracy, scalability, and real-time response capabilities in large-scale network environments. A distributed big data architecture is integrated with advanced deep neural networks to process high-dimensional network traffic features, perform automated feature learning, and classify malicious activities using optimized training and validation strategies. The proposed framework is evaluated using benchmark intrusion detection datasets and simulated real-world network traffic scenarios to ensure robustness and generalizability. Experimental findings demonstrate that the proposed approach achieves superior detection accuracy, lower False-Positive Rates, and improved processing efficiency compared with conventional machine learning-based intrusion-detection systems. The integration of deep learning and big data analytics provides a scalable and adaptive solution for intelligent threat detection in computer networks, contributing to the development of next-generation cybersecurity systems capable of addressing evolving and sophisticated cyber attacks.