New Approach for Network Threat Detection and Prevention Using Real-time Data Analysis and Deep Learning

Abstract

This research paper proposes a new approach for network threat detection and prevention using real-time data analysis and deep learning. The proposed approach utilizes isolation forests to effectively identify outliers. Isolation forests have gained popularity due to their efficacy in countering cyber threats, characterized by their speed and efficiency. The aim of the approach is to detect violations and their rapid responsiveness with minimal latency. A critical finding of the paper is that the proposed approach with deep learning techniques exhibit superiority in the learning of complex representations when compared to traditional techniques especially in terms of accuracy.