## 2 Related Work  
While the research and development of edge and IoT system security is primarily in its early stages, numerous researchers have reviewed existing IoT security countermeasuresin recent years to provide a roadmap for future work. Due to the diverse nature of IoT network hardware, intruders may create dynamic threats to take control of authorized communications or hardware devices. This section briefly examines the current state-of-the-art papers on IoT and edge security, highlighting recent attacks, threats, and countermeasures. A quick summary of these analyzed articles can be found in Table 1, including their focus, issues discussed, countermeasures described, and the future challenges provided. The authors of [28] explored several critical issues related to IoT security within edge computing, such as resource constraints, insufficient security, high latency in cloud computing, and the complexity and heterogeneity of IoT devices. They underscored the importance of securing the edge layer, developing robust security solutions for edge devices, and ensuring secure communication between network components. The authors also emphasized the need for lightweight protocols and secure operating systems tailored for resource-constrained IoT devices. A comprehensive survey on IoT security by the authors of [29] identified critical threats like privacy issues, authentication challenges, and information storage vulnerabilities. They categorized various physical, network, middleware, and gateway attacks. To address these challenges, the authors proposed scalable and adaptable security solutions leveraging technologies like blockchain, fog computing, and ML to enhance security and privacy while addressing scalability and resource constraints. Focusing on secure healthcare data aggregation and transmission in IoT environments, the authors of [30] identified significant security and privacy concerns, such as data aggregation risks, transmission security, and data integrity issues. They suggested countermeasures, including data encryption, privacy preservation mechanisms, and robust authentication protocols. The potential of edge and fog computing to reduce latency and improve data processing efficiency was also highlighted, which is crucial for securely handling large volumes of healthcare data. The application of ML and DL methods for IoT security was explored by the authors of [31], addressing the complexity and vulnerability of IoT systems. They discussed various attack surfaces and security threats, such as eavesdropping, DDoS attacks, and data tampering. The authors proposed leveraging supervised, unsupervised, and reinforcement learning techniques for anomaly detection and predictive analytics. Emphasizing the importance of scalable and adaptable ML/DL models, they aimed to handle the dynamic nature of IoT environments and ensure data privacy and security during processing. The works reviewed in [32] integrated LoRa technology with edge computing to tackle IoT challenges. They highlighted the limitations of cloud-based computing, the need for low-power, long-range communication, and scalability issues. The proposed edge computing architecture aims to reduce latency by processing data locally. The discussion also included application-specific enhancements in smart cities, industrial IoT, and smart agriculture, stressing regulatory compliance and implementing security measures like AES-128 encryption as essential for ensuring data integrity and confidentiality. Addressing secure data analytics in edge computing, the authors of [33] focused on the trade-offs between security and efficiency. They examined the trustworthiness of networked devices, usage privacy, and correctness of data computation as significant challenges. Proposing lightweight security mechanisms, effective trust management models, and privacy-preserving techniques, the authors aimed to ensure secure data analytics.