Mitigating Cyber Threats at the Network Edge

Abstract

The easy exploitation of IoT devices with limited security, compute and processing power has enabled hackers to carry out sophisticated attacks. Many research studies have highlighted the benefits of utilising artificial-intelligence based models in DDoS detection, but emphasis has not been placed on quantitative measurements of compute requirements for Machine Learning and Deep Learning algorithms used for DDoS detection, especially in the inference or detection stage. This research aims to fill the gap by performing quantitative measurement and comparison of various lightweight ML and DL algorithms, as well as design a lightweight collaborative framework capable of DDoS detection close to the source of the attack.