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The use of web-enabled healthcare analytics has broadened access to machine learning (ML)- and AI-driven cloud models, but it has also created privacy and security challenges. Federated learning (FL) has been used to address data privacy issues; however, deployments of current FL architectures rely on centralized aggregation approaches, thereby creating a single point of failure (SPoF), as a successful adversarial attack on the global model during training or inference can compromise the entire system. These approaches also assume homogeneous data distributions across clients and overlook the constraints and diversity of web-based analytics. To address those limitations, traditional blockchain-based FL systems incorporated distributed ledgers to record model updates and artifacts. However, using the chain as a data ledger to record model artifacts and logs increases consensus overhead and coordination costs. This paper introduces Blockchain-based Clustered Federated Learning (BCFL), an architecture-diverse and cluster-based FL framework. Our approach is coordinated by a lightweight permissioned ledger that eliminates the trusted central aggregator while preserving utility, robustness, and verifiable provenance in web-based healthcare analytics. BCFL records compact provenance metadata on-chain while keeping model parameters off-chain. In addition, by distributing trust across clusters, the design reduces the transfer of adversarial attacks across models by limiting the impact of malicious updates during training and improving reliability at inference time. Experiments on real-world healthcare data and other benchmarks show that BCFL improves the performance of trained AI/ML models and reduces attack success rates compared with several FL baselines. 

The medical field has garnered significant attention in the realm of cybersecurity. This project addresses that attention by performing big picture-oriented research on medical devices and uncovered bad practices, vulnerabilities, and usage hazards. It also built relationships with Army medical personnel, the FBI, WellStar, and strengthened ties with Augusta University. 

In this paper, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed’s capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed’s security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. 

Facial Recognition Systems (FRS) have become one of the most viable biometric identity authentication approaches in supervised and unsupervised applications. However, FRSs are known to be vulnerable to adversarial attacks such as identity theft and presentation attacks. The master face dictionary attacks (MFDA) leveraging multiple enrolled face templates have posed a notable threat to FRS. Federated learning-based FRS deployed on edge or mobile devices are particularly vulnerable to MFDA due to the absence of robust MF detectors. To mitigate the MFDA risks, we propose a trustworthy authentication system against visual MFDA (Trauma). Trauma leverages the analysis of specular highlights on diverse facial components and physiological characteristics inherent to human faces, exploiting the inability of existing MFDAs to replicate reflective elements accurately. We have developed a feature extractor network that employs a lightweight and low-latency vision transformer architecture to discern inconsistencies among specular highlights and physiological features in facial imagery. Extensive experimentation has been conducted to assess Trauma’s efficacy, utilizing public GAN-face detection datasets and mobile devices. Empirical findings demonstrate that Trauma achieves high detection accuracy, ranging from 97.83% to 99.56%, coupled with rapid detection speeds (less than 11 ms on mobile devices), even when confronted with state-of-the-art MFDA techniques.