Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking
This work tackles security challenges for IoT systems, which are increasingly pervasive in daily life, but it is incremental as it builds on existing technologies without introducing a novel method.
The paper addresses critical security threats in the Internet of Things (IoT) by proposing a new secure-by-design vision that leverages machine learning and software-defined networking for proactive and adaptive threat mitigation, but does not report specific experimental results or concrete numbers.
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.