Abstract

This Ph.D. dissertation presents a comprehensive investigation into addressing security and reliability challenges in embedded and Cyber-Physical Systems (CPS). Our research leverages advanced machine learning techniques such as Graph Neural Networks (GNN) to develop novel methodologies for cross-layer security analysis.This dissertation addresses the growing risk posed by the globalization of the Integrated Circuit (IC) supply chain, whereby the majority of the design, fabrication, and testing processes have been outsourced to untrusted third-party entities across the globe. This development has significantly increased the threat of malicious modifications, known as Hardware Trojans (HTs), being inserted into Third-Party Intellectual Property (3PIP). HTs pose a substantial risk to IC integrity, functionality, and performance. Despite numerous HT detection methods proposed in existing literature, most limitations include reliance on a golden reference circuit, lack of generalizability, limited detection scope, low localization resolution, and manual feature extraction and property definition. Furthermore, the equally important task of HT localization has been neglected. This research proposes an innovative, golden reference-free method for HT detection and localization at the pre-silicon stage of IC development, employing models based on GNN. The circuit design is converted into a graph that is an intrinsic data structure for hardware design and captures the computational structure and data dependencies. We develop a graph classification model to distinguish between HT-free and circuits infected with known or even unknown HTs. To push the boundaries further, we extract node attributes from the HDL code and devise a Graph Convolutional Network (GCN) that facilitates automatic feature extraction, enabling the classification of nodes as either Trojan or benign. This methodology offers an automated approach to HT detection and localization, relieving designers of the need for time-consuming manual code review. The developed method achieves exceptional performance in detecting HT-infected circuits and locating the HT. The approach outlined in this dissertation sets a new benchmark for HT detection and localization, offering a scalable, efficient, and highly accurate tool for securing the pre-silicon IC supply chain.This dissertation expands to encompass the challenges facing IP piracy. The productivity gap, coupled with time-to-market pressure, has led to increased interest in hardware Intellectual Property (IP) core design within the semiconductor industry, dramatically reducing design and verification costs. Recognizing these challenges, this dissertation proposes a novel IP piracy detection methodology, modeling circuits and assessing similarity between IP designs. Contrary to traditional methods that embed a signature within the circuit design, our method does not introduce additional hardware overhead, nor is it vulnerable to removal, masking, or forging attacks. This approach effectively exposes IP infringements, even when the original IP is complicated by the adversary to deceive the IP owner. To represent the circuit accurately for modeling, we translate the hardware design into a data-flow graph due to similar data types and properties and subsequently model it using state-of-the-art graph learning methods. This approach effectively complements the GNN-based techniques proposed earlier in this dissertation, presenting a robust and comprehensive suite of solutions for security and reliability challenges in the semiconductor industry. Moving to the CPS domain, the dissertation addresses security challenges in IoT systems through the development of adaptive anomaly detection methods. The first proposed approach utilizes IoT sensor data and fog computing to ensure data integrity and detect anomalous incidents. The proposed methodology incorporates our sensor association algorithm, LSTM neural networks, and Gaussian estimation for real-time anomaly detection. The dissertation further extends the research to multi-modal data fusion, where the integration of sensor and communication data using GNN enables improved anomaly detection, source identification, and recovery in IoT systems.Overall, this dissertation showcases the application of advanced techniques such as GNN and machine learning in enhancing security and reliability in hardware design and IoT systems. The proposed methodologies for anomaly detection, hardware Trojan detection, IP piracy detection, and cross-layer security analysis contribute to advancing the state-of-the-art in ensuring the integrity and security of critical systems in the digital era.