Securing Things in the Healthcare Internet of Things

Abstract

The Internet of Things (IoT) has had a positive impact on e-health, assisted living, human-centric sensing and wellness. Recently this interconnection has been referred to as Healthcare IoT (H-IoT). Real-time monitoring based on the information gathered from the connected ‘things’ provides large scale connectivity and a greater insight into patient care, individual habits and routines. While the benefits of introducing this paradigm into healthcare are conspicuous, the underlying security vulnerabilities and threats of the infrastructure and devices cannot go unaddressed. H-IoT is set to impact society significantly, and with attackers already exploiting the IoT in a myriad of ways, it is inevitable that the IoT will become the most vulnerable area of cyber security. Securing these ‘things’ in H-IoT requires a multi-faceted approach. A multi-agent approach to advanced persistent threat detection is conveyed with the use of machine learning for predictive analytics: identifying security vulnerabilities, identifying patterns in order to make predictions and identify outliers.