## 2 Related Work
They also emphasized the need for scalable security frameworks and the integration of ML to enhance security measures in edge computing environments. The research conducted in [11] analyzed security and privacy issues in edge computing-assisted IoT. Highlighting new attack surfaces introduced by the distributed nature of edge computing, they noted vulnerabilities due to limited computational resources and data proximity to end-users. The authors suggested solutions, including scalable and lightweight security mechanisms, dynamic trust management systems, and standardization protocols to enhance security and privacy in EC-assisted IoT systems. Exploring the potential of reinforcement learning (RL) for IoT security, the authors of [34] discussed various IoT security challenges, including diverse attack vectors and resource constraints. They highlighted RL's ability to adapt parameters and dynamically solve optimization problems with minimal information. They proposed RL-based security solutions that scale to handle many IoT devices and massive data volumes while addressing real-time adaptation and data privacy concerns. The authors of [35] surveyed AI methods for securing IoT services in edge computing. Identifying the vulnerabilities of edge nodes, including their distributed layout, limited computational resources, and heterogeneous environments, they proposed integrating AI with blockchain to enhance IoT security. This integration addresses the high computation and communication costs and evolving threat adaptation, and it ensures efficient AI-based security schemes. Examining DL-based security behavior in IoT environments, in ref. [36], the authors focused on security and privacy concerns due to limited resources and the ad hoc nature of IoT systems. They discussed the complexity of IoT systems and the need for advanced security methods. The authors proposed developing resource-efficient and adaptable DL models, handling heterogeneous data, and implementing lifelong learning to adapt to new security threats continuously. The authors of [37] reviewed IoT security threats and applications, categorizing physical, software, network, and encryption attacks. They highlighted challenges like battery consumption, limited memory, and open-range operations. The authors suggested integrating fog computing, ML, edge computing, and blockchain technologies to enhance IoT security, emphasizing scalability, resource constraints, interoperability, and real-time adaptation. Conducting a comprehensive survey on cybersecurity in IoT-based cloud computing, in ref. [38], the authors identified vital security concerns such as data breaches, data loss, unauthorized access, network vulnerabilities, and insider threats. They proposed countermeasures, including robust data encryption, multi-factor authentication, intrusion detection systems, and user education programs. The research in ref. [39] surveyed security vulnerability analysis, discovery, detection, and mitigation in IoT devices. The authors discussed the IoT architecture, potential attack surfaces, and methodologies for identifying and detecting vulnerabilities. Mitigation strategies, such as side-channel signal analysis, policy-based mechanisms, secure firmware updates, and robust encryption methods, were reviewed. Providing an extensive survey on security architectures for edge computing-based IoT systems, [PERSON] and [PERSON] [19] addressed issues related to resource management, security and privacy, and advanced communication technologies such as 5G. They proposed future research directions focused on balancing security and efficiency, creating effective trust models, ensuring usage privacy, supporting mobility and scalability, designing lightweight security mechanisms, and ensuring verifiable computation. The authors of [40] examined edge-computing architectures for IoT applications, emphasizing the importance of addressing the latency and bandwidth issues inherent in cloud-centric models.