# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 2 Literature Review
Another mechanism presented in [26] involves the implementation of a black-hole attack and introduces a corresponding prevention method for the AODV routing protocol within WSNs. The proposed prevention technique is centered on identifying the shortest paths between a non-compromised source and destination, as depicted in the diagram. It is important to note that both the source and destination nodes are presumed to be trustworthy and not compromised. Our approach involves incorporating the detection of malicious paths within the route-discovery phase of the AODV routing protocol. A fuzzy-logic-based technique was proposed in [27] to tackle the issue of gray-hole attacks targeting WSNs nodes integrated into IoT systems. The IoT system incorporates WSN by establishing connectivity through a router to amass data. By leveraging the deployed sensors, the nodes can detect attacks within the IoT framework. Each node within the IoT network is systematically organized, serving various functions. This organization is underpinned by connecting users and clients to a central controller, the base station, facilitating IoT connectivity. The intricate architecture intrinsic to IoT renders it susceptible to gray-hole attacks. The sensor nodes situated within the WSN play a pivotal role in detecting attacks occurring within the IoT domain. Routing protocols are pivotal in both WSN and IoT, effectively guiding the decision-making process for packet forwarding among nodes. Within the realms of IoT and WSN, two primary categories of routing protocols exist: proactive and reactive. Proactive routing protocols process network information, while reactive routing protocols manipulate the network's structure to facilitate efficient packet routing. In [28], a system for detecting intruders in WSNs and responding with an intelligent mobile robot was described. This system employs an unsupervised neural network for intrusion detection, focusing on identifying changes over time using a Markov model. Once an intrusion is detected, a robot is dispatched to the affected area for further investigation. The reported results indicate an approximate detection rate of 85%. Given that WSN nodes are often deployed in harsh and unattended environments, where attackers may compromise a subset of nodes, compromised sensor nodes can potentially transmit incorrect data to the central sink. To address this concern, a malicious-node-detection mechanism is proposed in [29] for hierarchical WSNs, where nodes gather and share data with neighboring nodes. This mechanism utilizes a feed-forward ANN technique. The authors assert that their proposed approach effectively identifies malicious nodes, even when up to 25% of sensor nodes have been compromised. In [30], the author placed a significant emphasis on addressing the challenge of detecting data anomalies in WSN. To tackle this issue, a convolutional neural network (CNN) model was meticulously crafted, leveraging the distinctive features of the marked mode and the deep neural network architecture to effectively identify anomalous data patterns. Throughout the study, the author introduced three innovative network models and conducts a comparative evaluation against a previously utilized CART (classification and regression trees) model. The assessment of these models was based on performance metrics such as detection accuracy (DA), true-positive rate (TPR), and precision (PRE). The experimental findings unequivocally demonstrate that the three models introduced in this research consistently outperform the CART model, with the M2 model exhibiting the highest level of performance. The author of [31] introduced a novel approach by utilizing a hybrid ANN method to identify uncommon elements within the framework of a \"smart home\" system. This research marked the first instance of employing a hybrid ANN technique in the domain of detecting irregularities within \"smart home\" or building-automation systems.