A novel framework for predicting network failure and network intrusion using ML tecniques

Protecting the next-gen communication networks from failure and cyber attacks are of utmost priority to the network service providers. In this regard, predicting network failure and network intrusion is vital towards developing an intelligent preventive mechanism against such network failure and attacks. This research project will develop algorithms that can predict network failures and attacks (in an end-to-end packet-optical network) using state-of-the-art machine learning techniques. Besides, our model will dynamically reserve network resources to protect the network traffic subject to such potential failure or attack. These proposed predictive and resource-friendly dynamic allocation schemes will help the carriers protect their network traffic from physical failure and cyber attacks.