Publication Date

2023

Document Type

Thesis

Committee Members

Fathi Amsaad, Ph.D. (Advisor); Lingwei Chen, Ph.D. (Committee Member); Michael L. Raymer, Ph.D. (Committee Member); Anton Netchaev, Ph.D. (Committee Member)

Degree Name

Master of Science (MS)

Abstract

The Industrial Internet of Things (IIoT) refers to a set of smart devices, i.e., actuators, detectors, smart sensors, and autonomous systems connected throughout the Internet to help achieve the purpose of various industrial applications. Unfortunately, IIoT applications are increasingly integrated into insecure physical environments leading to greater exposure to new cyber and physical system attacks. In the current IIoT security realm, effective anomaly detection is crucial for ensuring the integrity and reliability of critical infrastructure. Traditional security solutions may not apply to IIoT due to new dimensions, including extreme energy constraints in IIoT devices. Deep learning (DL) techniques like Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) have been the focus of recent research to increase the precision and effectiveness of anomaly identification. This Thesis initially investigates a unique hybrid DL-enabled approach that provide the needed security analysis before successful attacks are launched against IIoT infrastructure. For that, different hybrid models are developed, trained, tested, and validated using Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Short-Term Memory (LSTM), Autoencoder, and XGBoost algorithms. Experimental results show that the proposed XGBoost ML model exhibits the highest performance, as compared to other models, and excels across multiple metrics, including recall, precision, F1-score, and false alarm rate (FAR). The results also show that hybrid CNN+GRU model is closely behind, which exhibited strong performance in accurately detecting anomalies in encrypted IoT traffic. Specifically, Our experimental results show that the developed hybrid CNN+GRU model outperforms the others, achieving an accuracy of 94.94%, a recall of 92.29%, a precision of 98.49%, an F1 score of 95.24%, and a low false alarm rate of 0.001. However, it is essential to note that the hybrid model requires a longer convergence time, indicating a trade-off between performance and computational efficiency. Notably, individual CNN and GRU models also showcase strong performance as time-efficient alternatives. In conclusion, our adopted comprehensive dataset and rigorous evaluation proves that we have developed practical deep-learning approaches to obtain an accurate measure for an efficient IIoT anomaly detection framework. Finally, as a future work, this study highlights the significance of selecting the appropriate model for anomaly detection in IIoT systems. For that, XGBoost and CNN+GRU showcase of future research and the potential for achieving high accuracy and effectively identifying anomalies. The codes and used developed data sets of this research are attached in the appendix section at the end of this thesis to guide future works in developing advanced hybrid architectures and optimizing computational efficiency to enhance the security of IIoT systems further.

Page Count

120

Department or Program

Department of Computer Science and Engineering

Year Degree Awarded

2023

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