TOWARD ASSURANCE AND TRUST FOR THE INTERNET OF THINGS

Abstract

Kevin Ashton first used the term Internet of Things (IoT) in 1999 to describe a system in which objects in the physical world could be connected to the Internet by sensors. Since the inception of the term, the total number of Internet-connected devices has skyrocketed, resulting in their integration into every sector of society. Along with the convenience and functionality IoT devices introduce, there is serious concern regarding security, and the IoT security market has been slow to address fundamental security gaps. This dissertation explores some of these challenges in detail and proposes solutions that could make the IoT more secure. Because the challenges in IoT are broad, this work takes a broad view of securing the IoT. Each chapter in this dissertation explores particular aspects of security and privacy of the IoT, and introduces approaches to address them. We outline security threats related to IoT. We outline trends in the IoT market and explore opportunities to apply machine learning to protect IoT. We developed an IoT testbed to support IoT machine learning research. We propose a Connected Home Automated Security Monitor (CHASM) system that prevents devices from becoming invisible and uses machine learning to improve the security of the connected home and other connected domains. We extend the machine learning algorithms in CHASM to the network perimeter via a novel IoT edge sensor device. We assess the ways in which cybersecurity analytics will need to evolve and identify the potential role of government in promoting needed changes due to IoT adoptions. We applied supervised learning and deep learning classifiers to an IoT network connection log dataset to effectively identify varied botnet activity. We proposed a methodology, based on trust metrics and Delphic and Analytic Hierarchical Processes, to identify vulnera¬bilities in a supply chain and better quantify risk. We built a voice assistant for cyber in response to the increased rigor and associated cognitive load needed to maintain and protect IoT networks

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