Texas A&M University – Central Texas

Regulatory authorities worldwide are developing cybersecurity standards and data protection regulations related to Internet of Things (IoT) devices to protect user information. As smart IoT technologies evolve, the implementation of such regulations and standards is highly crucial for allowing the secure storage, processing, and transfer of data. Given the ongoing research and development of smart IoT technologies across various domains, we aim to investigate whether critical compliance standards are adequately addressed in existing academic research. Through an in-depth analysis utilizing topic modeling and large language model (LLM) approaches, we identified the top five researched areas and their relationship with relevant regulations. We identified that while substantial progress has been made in addressing the technical challenges associated with IoT privacy, there remains an urgent need for research that bridges these solutions with socio-technical systems and compliance. Our research emphasizes the importance of adopting privacy-by-design at the initial stages of IoT implementation. Additionally, we determined research gaps in existing studies and proposed future research directions emphasizing the development of IoT frameworks that are not only technologically robust but also align with evolving legal compliance standards.

The exponential growth of android-based mobile IoT systems has significantly increased the susceptibility of devices to cyberattacks, particularly in smart homes, UAVs, and other connected mobile environments. This article presents a federated learning-based intrusion detection framework called AndroIDS that leverages system call traces as a personalized and privacy-preserving data source. Unlike conventional centralized approaches, the proposed method enables collaborative anomaly detection without sharing raw data, thus preserving user privacy across distributed nodes. A generalized system call dataset was generated to reflect realistic android system behavior and serves as the foundation for experimentation. Extensive evaluation demonstrates the effectiveness of the FL model under both IID and non-IID conditions, achieving an accuracy of 96.46 % and 92.87%, and F1-scores of 89% and 86%, respectively. These results highlight the model's robustness to data heterogeneity, with only a minor performance drop in the non-IID case. Further, a detailed comparison with centralized deep learning further illustrates trade-offs in detection performance and deployment feasibility. Overall, the results validate the practical applicability of the proposed approach for secure and scalable intrusion detection in real-world mobile IoT scenarios.

As IoT device adoption grows, ensuring cybersecurity compliance with IoT standards, like National Institute of Standards and Technology Interagency (NISTIR) 8259A, has become increasingly complex. These standards are typically presented in lengthy, text-based formats that are difficult to process and query automatically. We built a knowledge graph to address this challenge to represent the key concepts, relationships, and references within NISTIR 8259A. We further integrate this knowledge graph with Retrieval-Augmented Generation (RAG) techniques that can be used by large language models (LLMs) to enhance the accuracy and contextual relevance of information retrieval. Additionally, we evaluate the performance of RAG using both graph-based queries and vector database embeddings. Our framework, implemented in Neo4j, was tested using multiple LLMs, including LLAMA2, Mistral-7B, and GPT-4. Our findings show that combining knowledge graphs with RAG significantly improves query precision and contextual relevance compared to unstructured vector-based retrieval methods. While traditional rule-based compliance tools were not evaluated in this study, our results demonstrate the advantages of structured, graph-driven querying for security standards like NISTIR 8259A.

This proposal aims to explore the impact of service-learning initiatives in developing critical thinking skills in undergraduate business students. We also propose a novel tracking framework to help business schools measure and quantify the societal impact – an increasingly vital criterion for Association of Advance Collegiate Schools of Business (AACSB) accreditation. This approach not only enables institutions to align their service-learning programs with AACSB standards but also maximizes their contributions to both student development and community well-being.

Invasive mold infections (IMI) can lead to severe morbidity and mortality, but routine public health surveillance is lacking. Although extensive evaluation is needed for clinical diagnosis, case classification prediction models may inform surveillance efforts, which are essential to better characterize epidemiologic trends and assess the value of a more inclusive IMI case definition. We modeled medical record data of potential IMI cases from 4 medical centers in Houston, Texas, during September 2016 to August 2018. We used least absolute shrinkage and selection operator and random forest machine learning methods to identify key host and clinical factors, mycological evidence, diagnostics, and health care exposures predictive of IMI case versus noncase status using both conventional and novel definitions. We assessed feature importance by measuring each variable's impact on prediction error using leave-one-covariate-out and permutation feature importance approaches. Receipt of systemic antifungal medication, hospital billing codes related to IMI, and positive pulmonary histopathology results were identified as the most important predictors of IMI case status across all measures. Removal of these features from the models resulted in reductions to prediction accuracy ranging from 3.6% (95% confidence interval [CI], 3.2%-3.2%) to 7.6% (95% CI, 7.2%-8.0%). Some IMI risk factors, including cancer diagnosis and prolonged receipt of corticosteroid medications, worsened prediction in several assessments of feature importance. Features identified as important predictors of IMI case status using machine learning methods deviated from classic IMI risk factors. Our results will inform robust and feasible IMI case prediction models for public health surveillance.

This beginner’s guide is intended for plant biologists new to network analysis. Here, we introduce key concepts and resources for researchers interested in incorporating network analysis into research, either as a stand-alone component for generating