# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 1 Introduction  
Human developments invariably have certain shortcomings [1]. All the progress made in the realm of communication, encompassing WSN, internet of things (IoT), embedded systems, ad hoc networking, and similar innovations, can be attributed to human ingenuity. In this context, human ingenuity is oriented towards enhancing lifestyle comfort through applications including smart homes, efficient transportation, and remote communication, among others. Currently, WSNs are widely embraced for sensing and controlling remote applications. A WSN is a network that consists of several sensor nodes strategically placed across a large region to monitor and record various physical parameters. Temperature, wind speed, pressure, humidity, and a variety of other characteristics are all scattered throughout multiple sites. Importantly, sensor nodes in a WSN are battery-powered and may operate in remote regions where human access is restricted or impossible [2]. In WSNs, numerous sensor nodes continuously capture data and transmit the data to a selected sink node. These data are transmitted either directly from node to node or through intermediaries known as cluster heads. The sensor nodes communicate their data to the cluster-head nodes, which then forward this information to the base station (BS) for further communication [3, 4]. WSNs are structured with a layered design wherein each layer serves distinct functions critical for efficient data transmission and network operation. These layers, including thephysical, data-link, network, transport, and application layers, play pivotal roles in ensuring the reliable and secure operation of WSNs [5]. At the physical layer, which constitutes the lowest layer of the WSN architecture, the network faces several security challenges. Eavesdropping attacks, compromised node attacks, replication node attacks, and basic jamming attacks are primary concerns in this layer. These threats target the fundamental components of the network, such as nodes and communication channels, potentially compromising data integrity and confidentiality. At the data-link layer, attacks such as denial of service (DoS) attacks, collision attacks, unfairness attacks, and intelligent jamming are prominent concerns. This layer is responsible for managing communication between neighboring nodes and addressing issues related to data packet collision and channel access. Ensuring the integrity of data-link-layer protocols is crucial to maintenance of network reliability. The network layer introduces further security challenges, including sybil attacks, sinkhole attacks, spoofing attacks, and black-hole, gray-hole, and wormhole attacks. These attacks exploit vulnerabilities in routing protocols and can disrupt data transmission, hijack routing paths, or imperonate legitimate nodes, posing significant threats to network functionality. The transport layer is susceptible to attacks like flooding and desynchronization, which can impact the quality of service and the reliability of data delivery. This layer manages end-to-end communication and ensures data integrity, making it a crucial aspect of WSN security. Lastly, at the application layer, data collected by WSNs are processed and utilized for various applications. Ensuring the security and privacy of this data is essential. Attackers may attempt to manipulate or intercept application-layer data, necessitating the use of robust security measures to safeguard sensitive information. WSNs have emerged as a vital technology for a wide range of applications, from environmental monitoring and healthcare to industrial automation and surveillance. These networks consist of numerous small, self-governing sensor nodes that collaborate to collect and transmit data wirelessly, facilitating efficient data collection and dissemination. However, the distinct attributes of WSNs, including their limited resources, dynamic network topology, and vulnerability to diverse attacks, expose them to risks, particularly at the network layer.