Improving Security for the Internet of Things: Applications of Blockchain, Machine Learning and Inter-Pulse Interval

Abstract

The Internet of Things (IoT) is a concept where physical objects of various sizes can seamlessly connect and communicate with each other without human intervention. The concept covers various applications, including healthcare, utility services, automotive/vehicular transportation, smart agriculture and smart city. The number of interconnected IoT devices has recently grown rapidly as a result of technological advancement in communications and computational systems. Consequently, this trend also highlights the need to address issues associated with IoT, the biggest risk of which is commonly known to be security. This thesis focuses on three selected security challenges from the IoT application areas of connected and autonomous vehicles (CAVs), Internet of Flying Things (IoFT), and human body interface and control systems (HBICS). For each of these challenges, a novel and innovative solution is proposed to address the identified problems. The research contributions of this thesis to the literature can be summarised as follows:

• A blockchain-based conditionally anonymised pseudonym management scheme for CAVs, supporting multi-jurisdictional road networks.

• A Sybil attack detection scheme for IoFT using machine learning carried out on intrinsically generated physical layer data of radio signals.

• A potential approach of using inter-pulse interval (IPI) biometrics for frequency hopping to mitigate jamming attacks on HBICS devices.