Browsing by Author "Suriza Ahmad Zabidi, Ph.D"

Accessing healthcare services by several stakeholders for diagnosis and treatment has become quite prevalent owing to the improvement in the industry and high levels of patient mobility. Due to the confidentiality and high sensitivity of electronic healthcare records (EHR), the majority of EHR data sharing is still conducted via fax or mail because of the lack of systematic infrastructure support for secure and reliable health data transfer, delaying the process of patient care. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting, where the storing the data over the cloud do not remain secure all the time. Since the data are accessible to the end user only by using the interference of a third party, it is prone to breach of authentication and integrity of the data. This thesis introduces the development of a Patient-Centered Blockchain-Based EHR Management (PCBEHRM) system that allows patients to manage their healthcare records across multiple stakeholders and to facilitate patient privacy and control without the need for a centralized infrastructure. In addition, the proposed system ensures a secure and optimized scheme for sharing data while maintaining data security and integrity over the Inter Planetary File System (IPFS). Further, the proposed system introduces a sophisticated End to End Encryption (E2EE) functionality by combining the ECC (Elliptic Curve Cryptography) method and the Advanced Encryption Standard (AES) method. This is to enhance the security of system, reduce the computational power for memory optimization, and ensure authentication and data integrity. We have also demonstrated how the proposed system design enables stakeholders such as patients, labs, researchers, etc., to obtain patient-centric data in a distributed and secure manner that is integrated using a web- based interface for the patient and all users to initiate the EHR sharing transactions. Finally, the thesis enhances the proposed PCBEHRM system with deep learning artificial intelligence capabilities to revolutionize the management of the EHR and offer an add-on diagnostic tool based on the captured EHR metadata. Deep learning in healthcare now had become incredibly powerful for supporting clinics and in transforming patient care in general and is increasingly applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical methods for diagnosing several diseases. The proposed enhancement integrated deep learning feature is a developed lightweight solution that can detect 14 different chest conditions from an X-ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. The proposed diagnostic add-on tool focuses on predicting the 14 diseases to provide insight for future chest radiography research. Finally, the proposed system was tested in Microsoft Windows@ environment by compiling a smart contract prototype using Truffle and deploying it on Ethereum using Web3. The proposed system was evaluated in terms of the projected medical data storage costs for the IPFS on blockchain, and the execution time for a different number of peers and document sizes. The results show that the proposed system achieves a reduced storage cost of 73.4172% and a 76% in execution time in comparison to other proposed systems in the open literature. The Results of the study conclude that the proposed strategy is both efficient and practicable. The add-on deep learning diagnostic feature flags any present diseases predicted from the health records and assists doctors and radiologists in making a well-informed decision during the detection and diagnosis of the disease.

The rapid growth of the Internet of Thing (IoT) has increased the demand for lightweight and robust hardware security mechanisms. Physical Unclonable Functions (PUFs) have emerged as promising hardware security primitives for device authentication, cryptographic key generation, and counterfeit prevention by leveraging intrinsic manufacturing process variations. Their capability for on demand cryptographic key generation eliminates the requirement for persistent key storage, thereby enhancing security against key extraction and tampering. However, PUF responses are often affected by environmental noise, voltage fluctuations, and device aging, which can compromise their reliability and randomness. Furthermore, recent advances in Machine Learning (ML) have demonstrated the ability to model and predict Challenge–Response Pairs (CRPs) of conventional strong PUFs, posing significant security risks. PUFs can be implemented in Application-Specific Integrated Circuits (ASICs) or, at lower cost, in Field-Programmable Gate Arrays (FPGAs). FPGAs are widely used in IoT and embedded systems due to their programmability, partial reconfigurability, and faster time-to-market. This research presents the design, implementation, calibration, and evaluation of FPGA-based PUFs aimed at improving robustness against environmental variations and ML modeling attacks. Both Arbiter PUFs (APUFs) and Ring Oscillator (RO) PUFs were implemented on an Intel Altera Cyclone IV-E FPGA, with CRPs extracted in real time using a logic analyzer. To mitigate noise and improve consistency, PUF CRPs were processed using whitening and hashing techniques. The performance of the proposed PUFs was evaluated using key metrics including reliability, uniqueness, randomness, and correctness. Randomness was validated through the NIST Statistical Test Suite (STS), while reliability was assessed under variations in temperature, voltage, and device aging. The study also explores the synergy between Artificial Intelligence (AI) and PUFs for IoT security. AI enhances edge computing security through advanced threat detection, automated responses, and optimized resource management. More than 900,000 CRPs were collected for both APUF and RO PUF and evaluated using key metrics including randomness, reliability, and uniqueness. The APUF demonstrated near ideal characteristics with high entropy, reliability (97.99%), and uniqueness (49.99%), while the RO PUF showed similarly strong statistical quality and robustness with reliability of 98.01% and uniqueness of 49.99%. Machine learning resistance was assessed using Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and MultiLayer Perceptron (MLP) models. For APUF, LR and GB achieved low prediction performance (around 61%), whereas RF and MLP attained high accuracies up to 97%, reflecting their ability to model complex PUF behaviour. The higher prediction accuracy observed for complex models such as MLP and RF can be advantageously interpreted from an anomaly detection perspective, rather than purely as a weakness. In contrast, RO PUF prediction accuracies remained close to random guessing (below 50%), confirming strong resistance against learning based attacks and high robustness of the CRP data. Moreover, when integrating PUFs into existing FPGA architectures, design compatibility, required modifications, and interactions with other system functions must be carefully considered. These factors highlight the importance of robust design strategies to ensure reliable and scalable PUF implementations for practical hardware security applications.