CPS-Guard: Framework for Dependability Assurance of AI- and LLM-Based Cyber-Physical Systems

Cyber-Physical Systems (CPS) increasingly depend on advanced AI techniques to operate in critical applications. However, traditional verification and validation methods often struggle to handle the unpredictable and dynamic nature of AI components. In this paper, we introduce CPS-Guard, a novel framework that employs multi-role orchestration to automate the iterative assurance process for AI-powered CPS. By assigning specialized roles (e.g., safety monitoring, security assessment, fault injection, and recovery planning) to dedicated agents within a simulated environment, CPS-Guard continuously evaluates and refines AI behavior against a range of dependability requirements. We demonstrate the framework through a case study involving an autonomous vehicle navigating an intersection with an AI-based planner. Our results show that CPS-Guard effectively detects vulnerabilities, manages performance impacts, and supports adaptive recovery strategies, thereby offering a structured and extensible solution for rigorous V&V in safety- and security-critical systems.

Enhancing Alzheimer's Disease Prediction: A Novel Approach to Leveraging GAN-Augmented Data for Improved CNN Model Accuracy

Alzheimer's Disease (AD) is a neurodegenerative disease affecting millions of individuals across the globe. As the prevalence of this disease continues to rise, early diagnosis is crucial to improve clinical outcomes. Neural networks, specifically Convolutional Neural Networks (CNNs), are promising tools for diagnosing individuals with Alzheimer's. However, neural networks such as ANNs and CNNs typically yield lower validation accuracies when fed lower quantities of data. Hence, Generative Adversarial Networks (GANs) can be utilized to synthesize data to augment these existing MRI datasets, potentially yielding higher validation accuracies. In this study, we use this principle while examining a novel application of the SSMI metric in selecting high-quality synthetic data generated by our GAN to compare its accuracies with shuffled data generated by our GAN. We observed that incorporating GANs with an SSMI metric returned the highest accuracies when compared to a traditional dataset.