## 5 Methodology
### The Proposed S-2 EC-ERF Framework  
In response to the dual challenges of security and optimizing energy consumption in WSNs, this study introduces the S-2 EC-ERF framework. This innovative approach combines the strengths of the ERF-LET algorithm for node-level security and the ECP-LEACH for network-level energy efficiency. The S-2 EC-ERF framework addresses the inherent challenges faced by WSNs by concurrently fortifying the security posture of individual nodes and optimizing energy consumption at the network level. This section presents a comprehensive solution that leverages the unique capabilities of S-2 EC-ERF, thereby ensuring a delicate balance between security and energy efficiency. Applying security algorithms at the edge level offers several benefits:  
* Security algorithms deployed at the edge can analyze and respond to security threats in real-time without needing to send data to centralized servers for processing. This reduces the latency associated with sending data to a remote location for analysis and decision making. * By processing sensitive data locally at the edge, security algorithms can help maintain data privacy by reducing the need to transmit sensitive information to centralized servers. This minimizes the risk of data breaches and unauthorized access during data transmission. * Edge devices can distribute the computational load of security algorithms, thus allowing for the scalable and efficient processing of security tasks across a distributed network. This scalability is particularly beneficial in large-scale deployments where centralized processing may become a bottleneck. * Edge-based security algorithms can continue to operate even in the event of network failures or disruptions. By decentralizing security processing, the network becomes more resilient to outages, thereby ensuring continuous protection against security threats. Machine learning algorithms played a pivotal role at the node level, thus serving as an advanced defense mechanism against potential security threats. Tailored to detect and counteract sophisticated attacks, the ERF-LET algorithm works at the node level in real-time, thereby identifying DDoS attacks efficiently. The ERF-LET algorithm's resource-efficient design is well-suited for WSNs, thereby enhancing the security posture by minimizing the risk of single points of failure and ensuring timely identification and response to malicious  
Figure 2: Training ERF-LET, DT, LR, and AdaBoost on the generated data from NS3. activities. The ERF-LET algorithm was applied to every node in the network. Each node examined all the data received from the rest of the nodes to determine whether there was an attack or not. If an attack was diagnosed, the sending node was identified as an attack node, and the data received from the attack node were dropped. The operation of the S-2 EC-ERF framework involves the following steps, as shown in Figure 3:  
* ERF-LET Initialization: The ERF-LET algorithm is trained using relevant data to distinguish between normal behavior and potential security threats, particularly DDoS attacks. The algorithm is designed to capture patterns indicative of various attacks, thus making it a robust defense mechanism. * Deploying ERF-LET on each Node: The trained ERF-LET algorithm is deployed to every node within the WSN. Each node is equipped with the ERF-LET to enable local analysis and decision making. * Sending Data Between Nodes using the ECP-LEACH Protocol. Data are exchanged between nodes using the ECP-LEACH protocol, because it achieves the task of saving energy for each node. The ECP-LEACH strategically manages the sleep states of nodes, thereby utilizing a sophisticated threshold mechanism to transition nodes into a sleep state when their energy consumption surpasses predefined levels. The sleep scheduling module optimizes energy usage, thereby extending the overall network lifetime and ensuring efficient resource utilization.