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About Me

Recent PhD Graduate of Penn State University in the field of Computer Science and Engineering. I am a researcher with a focus on Safety and Security, having completed several research projects designing tools for fault tolerance and policy enforcement in Internet of Things (IoT) systems.

During my time as a graduate student, I had the opportunity to work on several projects that developed my skills in security research and overall programming processes, with a focus in security and machine learning. Designing the flexible fault handling tool IoTRepair taught me how to work with libraries and APIs in embedded systems in order to implement various automated functions, such as device restarts and rollbacks.

Creating the flexible components of IoTRepair also led into my ProvPredictor and IoTArmor projects, which focused on using provenance information to train predictive Machine Learning models that could adapt to environments to make predictions about behavior and infer the likely causes. ProvPredictor required I learn how to deal with scalability issues inherent to the diverse IoT environments and how to increase ML model efficiency and noise tolerance to make effective predictions. In developing IoTArmor, I learned the design principles of Root-Cause-Analysis and how to optimize search functions by both finding accurate costs and accounting for complications, such as frequent local minimums.

Research Interest

  1. Mobile and IoT System Security
  2. Intrusion and anomaly detection and prevention
  3. Machine Learning Models

Publications

[1]. Norris, Michael, et al. "IoTRepair: flexible fault handling in diverse IoT deployments." ACM Transactions on Internet of Things 3.3 (2022): 1-33.

[2]. Norris, Michael, et al. "IoTRepair: Systematically addressing device faults in commodity IoT." 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI). IEEE, 2020.

[3]. Norris, Michael, et al. "ProvPredictor: Utilizing Provenance Information for Real-Time IoT Policy Enforcement." International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles. Cham: Springer Nature Switzerland, 2024.