## 2 Related Work  
This section focuses on the implementation of artificial intelligence in the context of WSN security. How AI addresses security needs has been studied, and its impact on network performance has been evaluated. The majority of machine learning applications in security have focused on using intrusion detection techniques to gain insight into the flow of packets within a network [9; 10; 11; 12]. These machine learning methods contribute to network security by mitigating Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks. Additional assistance is provided in examining virus behavior and mitigating potential threats to data integrity, such as ransomware attacks [13]. Moreover, some machine learning algorithms help prevent authentication threats between WSN nodes. [PERSON] et al. in [14] discovered that more data were needed for machine learning algorithms to be effective in achieving network security and quickly identifying intrusions. Moreover, processing big amounts of data necessitated significant energy expenditures. Stated differently, there exists a trade-off between the greater computational load of the ML algorithm and the energy constraints of the WSN. In WSN settings, it has been suggested that machine learning techniques be deployed centrally to prevent this trade-off. The two energy efficiency protocols, Energy Efficient Sensor Routing (EESR) and Low Energy Adaptive Clustering Hierarchy (LEACH), were revised in [15]. In order to improve energy efficiency, a protocol based on the LEACH subset was combined with a Levenberg-Marquardt neural network (LMNN). The outcomes of the simulation showed that Sub-LEACH outperformed its competitors in terms of energy efficiency. Additionally, a support vector machine (SVM)-based intrusion detection system (IDS) was suggested for the purpose of anomaly detection. Adequate outcomes were attained in relation to energy efficiency, overall latency, and anomaly detection analysis. The research has mostly dealt with detecting DoS intrusions or power optimization. To make the network fault-tolerant and reliable, researchers [16] presented a hybrid DoS detection algorithm based on energy conservation. Nodes created a dominant connected group to maximize data transfer and avoid packet retransmission. Simulation results revealed that the network lifetime extended to 50%, the packet drop reduced to 50%, and the power usage was less than 3%. A novel Secure Deep Learning (SecDL) approach was developed in [17] with the aim of enhancing energy efficiency. An innovative encryption technique called OT-PRESENT has been developed to ensure robust security for the gathered data. SecDL successfully attained robust security measures, high-quality service delivery, and optimal energy utilization. The NS-3.26 network architecture yielded enhanced network lifetime, packet delivery ratio, throughput, encryption time, and delay. In [18], a trust-based approach to malicious node detection (WT-MND) in clustered WSNs was proposed. Simulation results showed that the WT-MND quickly identified and isolated malicious nodes, temporarily restored the trust of faulty nodes, and calculated a distinct trust update factor for each node based on its behavior without incurring additional energy consumption costs. In [19], an improved deep neural network approach to detect DoS attacks in wireless multimedia sensor networks (WMSNs) was proposed. The necessary parameters of the adaptive particle swarm optimization technique were selected, and the efficiency of this technique was determined by analyzing the packet transmission ratio, power consumption, latency, network length, and throughput. The ART2 neural network technology was implemented using the EESR protocol, which had been found to be a more effective approach in improving system lifetime. The main obstacle faced by current WSN technology is security. [PERSON] et al. [20] addressed the issue of intrusion detection in energy-efficient sensor networks by using neural perturbation technology.