## 1 Introduction  
The Internet of Things (IoT) is a comprehensive network of interconnected physical devices equipped with sensors, software, and various communication technologies, enabling them to communicate and share data over the Internet. Powered by smart devices, edge computing (EC), and big data analytics, IoT is transforming both business operations and the interactions between service providers and customers [1]. The number of IoT devices is estimated to nearly double, increasing from 15.9 billion in 2023 to over 32.1 billion by 2030. IoT-based services are rapidly being adopted across various industries and consumer markets, including healthcare, industrial automation, automotive, smart cities, logistics, and agriculture [2; 3]. The adoption of IoT has been further driven by the integration of advanced technologies such as 5G, AI, Blockchain, and EC [4; 5]. A typical IoT architecture, shown in Figure 1, consists of devices, sensors, and actuators in the perception layer, generating a large amount of data which requires further processing to enable intelligence for service providers and end users. The network layer transports data from the perception layer to the network via gateways that might perform preprocessing and often gather data from other edge devices. Authentication, encryption, malware protection, processing, and initial decision-making are carried out in this layer. The data processing is either carried out in the gateway or the cloud, based on the application and implementation. Cloud computing technology requires sensed data to be uploaded to centralized servers called data centers for further processing, and the results are transmitted back to the device layer. Such a centralized processing approach puts enormous pressure on the communication network regarding bandwidth, latency, and the vulnerability of data security [6]. Centrally located servers at data centers offer poor quality of service (QoS) in addition to the burden imposed on the communication networks, including the following:  
* Additional costs are involved due to inefficient utilization of bandwidth and network resources;
* Large-sized data drastically degrade network performance;
* Billions of connected devices on the IoT network make it difficult to manage data traffic; and
* Time-sensitive IoT applications are bound to become affected due to network-introduced latency [7; 8]. The idea behind \"edge computing (EC)\" is to minimize communication latency and bandwidth usage, enable real-time data analysis, reduce operational costs, enhance scalability, and improve service quality [9; 10; 11; 12]. Closer EC proximity to the data sources reduces transmission delays, packet loss, and high energy consumption [10; 13; 14]. Additionally, EC offers location-aware services and enhances resource allocation by shifting tasks from IoT devices with limited resources to more powerful edge servers [15]. Thus, EC is characterized by its heterogeneous distributed network architecture, large-scale data processing, parallel computing capabilities, and support for mobility services, including location tracking. However, EC increases vulnerability to cyberattacks and threats, as sensitive data are  
Figure 1: A typical IoT architecture. stored and processed in a distributed environment with limited resources, making it difficult to implement complex security algorithms [7; 16]. Figure 2 shows an edge-based IoT attack model outlining various threats and vulnerabilities specific to the edge computing environment in the Internet of Things (IoT) context [17]. Additionally, the dynamic nature of the edge in IoT networks makes them more susceptible to security attacks and difficult to protect. Mostly, the data security threats and attacks on EC architecture are placed under four categories, i.e., distributed denial-of-service (DDoS) attacks, side-channel attacks, malware injection attacks, and authentication and authorization attacks [18]. [PERSON] et al.