Big Data Analysis on Network Intrusion Detection using High Performance Deep Neural Networks

Abstract

The rapid evolution of the internet over decades has given rise to a significant increase in cyber-attacks, propelled by the growth of high-speed internet. This paper addresses the escalating threat by focusing on Network Intrusion Detection Techniques in big data environments, specifically utilizing high-performance deep neural networks for analysing intrusive data within the enhanced NSL-KDD dataset. The research emphasizes the application of the random forest algorithm to enhance accuracy in detecting intrusive attack data within large datasets. The proposed model is thoroughly evaluated using the enhanced NSL-KDD dataset, demonstrating superior performance compared to existing systems. The study contributes a comprehensive overview of the methodology, incorporating advanced techniques in deep neural networks and random forest algorithms. The results highlight the effectiveness of the proposed model in bolstering cybersecurity measures, offering a robust solution for the evolving landscape of cyber threats.