Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking

TL;DR

This paper discusses the importance of integrating machine learning and software-defined networking into IoT security to enable proactive, adaptive, and secure IoT systems for the future.

Contribution

It provides a comprehensive taxonomy, surveys current research, and outlines a roadmap for applying ML and SDN to enhance IoT security.

Findings

Survey of IoT security research

Identification of research challenges

Roadmap for future IoT security solutions

Abstract

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning and software-defined networking will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of machine learning and software-defined networking to address existing and next-generation IoT security threats.