# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 2 Literature Review
Through this method, the network aims to effectively manage instances of jamming and enhance the overall performance and reliability of the communication environment. The work presented in [19] proposed that a cluster-head node should select some surveillance nodes to calculate reputation and trust routes. A monitoring mechanism and anomaly-detection mechanism based on energy thresholds are used to detect errors generated by the attacked node or invalid data. This study ignored the trust rating of CH, which is an obvious disadvantage of this plan. The authors [20] proposed a trust-based low-power routing protocol (TEESR) that uses an authorization mechanism to reduce the probability of multiple malicious nodes occurring in adjacent areas. The node trust value determines the coverage radius and multi-path security route, and then the cluster head and the base station node make a comprehensive judgment to select the route with the higher security value. Although the proposed protocol performs well in dealing with external attacks, such as convergence holes and wormholes, it is powerless to deal with internal attacks. A new trust-aware secure routing protocol (TSRP) was suggested in [21] to prevent wormhole, selective-forwarding, and black-hole attacks. The protocol has good performance in terms of average packet loss rate and throughput, but its high energy consumption is a disadvantage. The authors of [22] investigated the impact of black-hole denial-of-service attacks on the general-purpose ad hoc on-demand distance vector (AODV) protocol. The evaluation encompassed normal AODV, BH_AODV, and D_BH_AODV variations. These attacks were found to considerably impede network operations. Countermeasures such as intrusion-detection systems (IDS) and digital signatures were employed to thwart black-hole attacks. Quality of service (QoS) metrics, including packet delivery ratio (PDR), delay, and overhead, were adopted to conduct a comparative analysis among the conventional AODV, BH_AODV, and D_BH_AODV protocols. The assessment took into account varying factors such as nodes, packet sizes, and simulation durations. The experimentation employed NS2 to emulate malicious protocols and featured a customized D_BH_AODV routing protocol. The results revealed that the D_BH_AODV approach substantially enhances PDR by 40% to 50% across different nodes and packets. Moreover, as the number of nodes and the number of packets increased, the delay was notably reduced, from 300 to 100 ms and from 150 to 50 ms, respectively. Node and packet configurations influenced overhead levels, which ranged from 1 to 3. The findings underscore the detrimental impact of black-hole attacks on network performance. However, the introduced D_BH_AODV strategy demonstrated efficacy in enhancing QoS. This improvement was achieved through the identification and circumvention of black-hole nodes, which effectively ensures more secure and reliable communication pathways. An effective approach proposed in [6] was used to identify black-hole attacks with notable efficiency. The occurrence of a black-hole attack leads to notable enhancements in crucial network parameters such as TH, TND, NRL, PDR, and PLR. To achieve this enhancement, the method presented employs a combination of techniques, including the K-nearest neighbors (KNN) algorithm for clustering, the beta distribution, [PERSON]'s mental logic, and fuzzy inference to compute trust levels. These calculated trust levels contribute to the establishment of node reputation, which is subsequently evaluated by a trust server. Ultimately, guided by the reputation table, the cluster master undertakes periodic actions. A detailed analysis presented in [23] focused on performing a forensic examination of ad hoc IoT networks that employ the AODV routing protocol. This analysis was conducted within the context of black-hole attacks, a form of denial-of-service attack that poses a substantial threat to IoT networks.