Design and Development of AI-Enabled Framework for IoT Networks

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The Internet of Things (IoT) is having a profound impact on our daily lives, and will be the foundation of our critical infrastructure and the basis for emerging and future smart services. It is a network of devices with computing, communication, sensing and actuating capabilities that interact in a feedback loop with the possibility of human intervention. As IoT proliferation continues, so do attacks such as denial of service that provide hackers access to streaming video feeds or taking control of autonomous vehicles, and hence a breach in the intended behavior of IoT devices could prove catastrophic. The diversity of IoT application domains lead to many unique challenges, and my research program will address machine intelligence, security, and human-in-the-loop challenges by drawing from the disciplines of machine learning, security and privacy, middleware, and human-machine interfacing to develop a framework for the secure development and analysis of IoT applications to facilitate the ubiquitous deployment of IoT networks. IoT networks involve a large number of devices that interact with the physical world, human users, and the cloud in rich and complex ways. This will raise several challenges including: scalability, increasing resource-demanding protocols such as video processing on devices, the role of edge devices, open network interfaces, and the secure communication between all devices while maintaining the privacy of users and their data. Recent news highlighted many opportunities for attacks on networked cars and implanted medical devices, and the case where Volkswagen was found to have programmed their software to cheat on emission tests revealed the potential for IoT devices to misrepresent themselves. Data is collected and processed in real-time, and critical decisions are based on this data (e.g., turn on the lights or an air conditioner, or apply brakes in a self-driving car), but data can be invalid for several reasons including: sensor failure, malicious users injecting incorrect data, or machine learning algorithms implemented incorrectly. To this end, the objective of this research is to investigate and develop solutions that address the above challenges into a framework for the development and analysis of IoT networks, including: machine learning algorithms as reusable components for analysis of network traffic for anomalous activity detection and prediction, novel security mechanisms at the application and network layers, and evaluate the tradeoffs between machine intelligence accuracy and performance, user experience, and security & privacy. The development of the proposed framework has a direct economic and societal impact, which is securing IoT networks and thus protecting enterprises and citizens from cyber security attacks and privacy invasion risks. My research program will train HQP in machine learning, middleware development, and IoT security to meet the demands for a skilled workforce in those areas.