Date of Award

5-2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

School

Computing Sciences and Computer Engineering

Committee Chair

Dr. Zhaoxian Zhou

Committee Chair School

Computing Sciences and Computer Engineering

Committee Member 2

Dr. Chaoyang Zhang

Committee Member 2 School

Computing Sciences and Computer Engineering

Committee Member 3

Dr. Bo Li

Committee Member 3 School

Computing Sciences and Computer Engineering

Committee Member 4

Dr. Ras Pandey

Committee Member 4 School

Mathematics and Natural Sciences

Committee Member 5

Dr. Sarah Lee

Committee Member 5 School

Computing Sciences and Computer Engineering

Committee Member 6

Dr. Sarbagya Ratna Shakya

Abstract

Security in the Industrial Internet of Things encounters various security issues but the main issues can be broken down into three core issues: Availability, Integrity, and Confidentiality. Security challenges generally tend to be caused by a failure of the system in one of these areas or cause a failure in one of these areas. Therefore researching scalable solutions to these security issues is prudent to explore methods that could be applied to large-scale industrial IIoT with tens to hundreds of devices as well as small-scale systems on a tiny factory floor comprising of just a few devices. In our research, we explore some of these methods focusing on the availability and integrity of the data. Our research intentions are to explore solutions to the Availability, Integrity, and Confidentiality issues individually and then bring it together to create an encompassing solution that solves all three problems. In our initial project, we delved into the concept of availability from a transmission standpoint. In doing so, we conducted experiments aimed at comprehending the limitations, characteristics, and behaviors of various transmission protocols applicable to over-the-counter IoT devices. These devices typically consist of simple MCU and sensor combinations, which are commonly found in small-scale IIoT setups, often deployed in noisy industrial environments. Our objective was to assess how these protocols performed under conditions resembling the real-world industrial setting, where noise is prevalent. We sought to understand the extent to which noise affected their operation and how closely they adhered to ideal operational parameters. The protocols we investigated included Xbee, Zigbee, Bluetooth Low Energy, Wi-Fi, and LoRa." In our second experiment, our objective was to investigate the feasibility of utilizing blockchain for ensuring data integrity, and whether it could be efficiently scaled down to run directly on IIoT devices. To achieve this, we successfully developed a functional proof of concept that not only met the stringent data integrity requirements and testing criteria but also demonstrated lightweight characteristics, enabling it to run directly on our IIoT devices. This implementation actively participated in the operation of the wireless sensor network that controls the IIoT devices. In the final experiment, we aimed to investigate and enhance the performance of our network security machine-learning model for intrusion detection by focusing on model selection. A significant aspect of our entire research endeavor is efficiency and lightweight operability, given that many components of our designed systems rely on low-powered computing systems such as the Raspberry Pi. In this context, the elimination of features that do not significantly contribute to intrusion detection in the network is essential to reduce the model’s complexity and resource requirements.

ORCID ID

https://orcid.org/0009-0001-7310-6178

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Available for download on Tuesday, June 03, 2025

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