AI-Based Network Traffic Analysis for Threat Hunting

Abstract

The focus of this study is to explore the effectiveness of advanced models like CNN and RNN to detect cyber threats on our network systems that are posted to us with every passing day. By using the CIC-DDoS 2019 dataset we test the possibility of detecting threats on network traffic in a realistic environment. This study tests both the limitations and strengths of neural networks for our case study. Our aim is to make a system efficient enough to improve itself over time and mitigate the threats posed in future by predicting them beforehand. This involves difficult testing of the CNN and RNN models to identify any patterns or anomalies that are indicate DDos attacks. This will work with the integration of AI with Cybersecurity and working on the real-world data and improve the accuracy and speed of these models to work efficiently. This will help make our environment resistant to the threats that will emerge with the advent of technology. These findings are intended to inform the development of more robust and autonomous AI systems that safeguard Internet while respecting user privacy.