The Internet of Things (IoT) revolutionizes connectivity, enabling innovative applications across healthcare, industry, and smart cities but also introducing significant cybersecurity challenges due to its expanded attack surface. Intrusion Detection Systems (IDSs) play a pivotal role in addressing these challenges, offering tailored solutions to detect and mitigate threats in dynamic and resource-constrained IoT environments. Through a rigorous analysis, this study classifies IDS research based on methodologies, performance metrics, and application domains, providing a comprehensive synthesis of the field. Key findings reveal a paradigm shift towards integrating artificial intelligence (AI) and hybrid approaches, surpassing the limitations of traditional, static methods. These advancements highlight the potential for IDSs to enhance scalability, adaptability, and detection accuracy. However, unresolved challenges, such as resource efficiency and real-world applicability, underline the need for further research. By contextualizing these findings within the broader landscape of IoT security, this work emphasizes the critical importance of developing IDS solutions that ensure the reliability, privacy, and security of interconnected systems, contributing to the sustainable evolution of IoT ecosystems.

Ensuring transparency and integrity in agricultural data management is a critical challenge as the sector increasingly relies on advanced technologies. The primary problem is maintaining data traceability and security throughout the supply chain. In this paper, we explore the use of directed acyclic graph (DAG) technology to address these challenges in agricultural environments. We have implemented the corresponding software to test the following three DAG‐based data storage and traceability scenarios: serverless, utilizing the interplanetary file system (IPFS), and a centralized server for data storage and transaction validation. Our results demonstrate that DAG and PoA ensure fast and secure transaction validation, crucial to maintaining trust and efficiency in the agricultural supply chain. We find that each scenario presents unique strengths and limitations, suggesting that a hybrid approach may offer the most robust solution for agricultural data management.