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With support from the National Science Foundation, The University of Washington Tacoma and North Dakota State University have developed scenario-based security curriculum and online showcase labs with interactive simulations and case studies across three progressive courses, revolutionizing cybersecurity education for Criminal Justice (CJ) professionals. By incorporating artificial intelligence into the curriculum, this project enhances CJ professionals’ capabilities. Our goal is to develop a skilled workforce of CJ professionals with cybersecurity and privacy knowledge, addressing the critical need for such cybersecurity expertise in CJ. Literature review, focus group survey results, course framework tailored for CJ professionals, example course modules, and implementation results are presented. 

The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed approach combines knowledge distillation and transfer learning to support heterogeneous IoT devices with varying computational capacities, ensuring efficient local training without compromising privacy. Blockchain technology is integrated to provide decentralized, tamper-resistant access control through Role-Based Access Control (RBAC), allowing only authenticated devices to participate in the federated learning process. This combination ensures data confidentiality, system integrity, and trust among devices. This framework’s performance was evaluated using the N-BaIoT dataset, showcasing its ability to detect anomalies caused by botnets such as Mirai and BASHLITE across diverse IoT devices. Results demonstrate significant improvements in intrusion detection accuracy, particularly for resource-constrained devices, while maintaining privacy and adaptability in dynamic smart home environments. These findings highlight the potential of this blockchain-enhanced federated learning system to offer a scalable, robust, and privacy-preserving solution for securing smart homes against evolving threats.