Publication Date

2023

Document Type

Thesis

Committee Members

Fathi Amsaad, Ph.D. (Advisor); Wen Zhang, Ph.D. (Committee Member); Huaining Cheng, Ph.D. (Committee Member)

Degree Name

Master of Science in Computer Engineering (MSCE)

Abstract

The Internet of Things (IoT) infrastructure encompasses smart devices and real-time sensors connected through the Internet, facilitating the exchange of large datasets among these devices. This interconnected network of IoT sensors generates a significant volume of data for processing and analysis by embedded IoT Edge Computing systems. IoT Edge Computing systems enable efficient real-time analysis and data communications. Furthermore, IoT Edge Computing emerges to enhance the overall efficiency of IoT applications, making them adept at handling the dynamic demands of AI-based and large data-driven applications. The integration of IoT Edge Computing introduces several unique research challenges. Unfortunately, IoT Edge Computing applications are increasingly integrated into non-secure physical environments, rendering them vulnerable to new cyberattacks. These cybersecurity threats can compromise the security and privacy of sensitive real-time information, potentially leading to life-threatening situations. In addition to the limited computational and storage capabilities of edge devices demanding resource-efficient algorithms for real-time analysis, the distributed nature of edge devices and the diverse range of data they generate pose challenges regarding data integrity, confidentiality, privacy, and availability. For that, detecting anomalies in IoT Edge Computing is crucial for ensuring essential real-time and life-critical applications' integrity, confidentiality, and availability. In this thesis, we address above mentioned challenges by proposing a novel hybrid knowledge-based federated learning approach. This approach enhances the security and efficiency of IoT Edge Computing applications and considers the specific constraints and requirements associated with the edge environment. By integrating knowledge distillation and federated techniques, our model ensures optimal resource utilization while maintaining robust security and privacy protocols. To execute the development and implementation of our models, we leverage advanced deep learning techniques, incorporating Convolutional Neural Networks (CNN) and complex transfer learning methods defined by architectures such as ResNet. We meticulously trained, tested, and validated the developed model to ensure its effectiveness in IoT Edge Computing. Our findings indicate that our hybrid model, incorporating federated learning principles and knowledge-based approaches, maintains a commendable global model accuracy of 94.90%. This demonstrates a significant improvement over existing models, such as the one proposed by Rahman et al. [1], which reported an accuracy of approximately 83.09% using a FL-based Intrusion Detection System (IDS) for IoT networks. The experimental results showcase the adaptability and resilience of our model in the face of Edge Computing challenges, contributing to the security and efficiency of the IoT infrastructure. In conclusion, our proposed knowledge-based federated learning approach ensures high AI accuracy for anomaly detection and optimizes communication efficiency across IoT Edge Computing applications. By addressing the unique challenges posed by Edge Computing, our model paves the way for secure and efficient IoT deployments in diverse real-time and life-critical applications.

Page Count

133

Department or Program

Department of Computer Science and Engineering

Year Degree Awarded

2023

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