# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 2 Literature Review
In contrast, the route-identification process in the AODV-black-hole strategy incurs higher costs in terms of detecting alternative routes to the destination. A machine-learning classification algorithm proposed in [15] for the detection of distributed denial of service attacks, including flooding, gray-hole, and black-hole attacks, within wireless WSNs. The investigation was carried out utilizing a dataset specific to WSNs, denoted as WSN-DS. In this evaluation, both accuracy and speed were taken into consideration as performance metrics. The outcomes of the analysis revealed that the J48 algorithm proved to be the most accurate and expeditious means of identifying gray-hole and black-hole attacks. Simultaneously, the random tree method emerged as the most accurate and swift approach for detecting flooding attacks. Specifically, in terms of speed, the J48 algorithm demonstrates efficiency, demanding an average processing time of 0.54 s per sample. In [16], an approach was proposed that consisted of four distinct components: network data collection (NDC), gray-hole detection (GD), gray-hole prevention (GP), and gray-hole reduction (GR). The GD and GR procedures are conducted separately during the execution of the proposed system. In contrast, execution of the remaining functionalities is dependent on the analysis of dependencies. Furthermore, the initiation of network data collection occurs exclusively when a network controller retrieves packets from nearby nodes. On the other hand, the prevention process is activated when a flow exceeds a specified threshold. The work done in [17] introduced an innovative lightweight encryption-based technique designed to counter sinkhole attacks in mobile wireless sensor networks (MWSNs). The primary focus of this proposed approach was to concurrently minimize energy consumption and enhance the precision of sinkhole-attack prevention. To accomplish this, a hybrid encryption algorithm was employed that integrated the robust blowfish algorithm for message encryption. This inclusion ensured both data integrity and the security of MWSNs against sinkhole attacks. Notably, the blowfish algorithm was chosen due to its resource-efficient nature and rapid processing, which render it highly suitable for MWSNs. To ensure the secure exchange of the blowfish key between the sender and the receiver, a strategic protocol was devised. At the initiation of the process, the sender employs the RSA algorithm to encrypt the blowfish key. This cryptographic approach guarantees the confidentiality and reliability of the key-sharing process. Subsequently, the recipient can effortlessly decrypt the message by employing its proprietary private key, ensuring the secure exchange of sensitive information. A hybrid spectrum management optimization (HSMO) technique was proposed in [18] for identifying and resolving jammed nodes within a network. This method involves the utilization of edge nodes, sub-edge nodes, and cluster heads to detect and address jammed nodes. These specific node types play a key role in identifying instances of node jamming. The process begins with the edge nodes, sub-edge nodes, and cluster heads being selected to participate in the jammed-node-detection procedure. As a result of this step, these nodes collectively contribute to the identification of jammed nodes. The edge node's role involves evaluating objective function 1 across packets received from the sub-edge node. This assessment occurs on distinct channels but with identical frequencies. The sub-edge node evaluates the second objective function based on signals received from the cluster head. In particular, it focuses on two consecutive packets it receives. If neither of these packets satisfies the stipulated objective function, the sub-edge node severs its connection with the cluster head, resulting in a loss of connectivity. The cluster head engages in the hybrid spectrum management optimization (HSMO) procedure to both detect and eliminate the identified jammed node.