Deep Learning For Cyber Security in the Internet of Things (IoT) Network

Abstract

The Internet of Things (IoT) is a swiftly evolving paradigm having the potential to transform the physical interaction between individuals and organizations. IoT has applications in multiple fields such as healthcare, education, resource management, and information processing to name a few. Many organizations rely greatly on technology, and most are changing their process into intelligent or smart solutions. Moreover, these networks are wireless, self-configuring, do not need preexisting infrastructure, and have a large unpredictable node movement; security becomes one of the most crucial concerns that need to be addressed. In this paper, we proposed an intrusion prevention method that uses a federated deep learning-based framework. A real IoT traffic dataset will be used to train the state-of-the-art graph neural network algorithm. A comparison will be carried out based on different experimental results. Finally, this work contributes to the security of IoT networks through the implementation of effective tools/techniques for timely IoT attack classification and mitigation.