ABSTRACT Cyber security is very important in Wireless Sensor Networks (WSNs) for securing the transfer of files from attackers. Cyber‐physical systems (CPs) are essential to monitor and observe the location of data in WSN. CPs are essential to monitor and track the location of data in a WSN. Many researchers have implemented different mechanisms to improve cybersecurity in WSN‐enabled CP. These mechanisms are effectively performed based on the mobile anchor node or mobility of the head node. This algorithm suffers from computational complexity. Some traditional cybersecurity systems suffer from data loss, important data theft, and information leakage. In addition, the CPs also suffer from service interference issues. Black holes, scheduling, gray holes, and flooding are some of the examples of common WSN attacks that damage the entire security system in WSN. The WSN has disadvantages such as low identification rates, high computing overhead, and increased false alarm rates. Conventional cybersecurity systems are required to decrease data redundancy and increase the data correlation for better data transformation. In this paper, a new cybersecurity system in WSN is developed to detect WSN intrusions effectively to enhance adaptability and security. The normal and anomalous information is gathered from online resources. Initially, the gathered information is given to the Adaptive and Attention serial Cascaded Ensemble Network (A‐ASCENet) for detecting various intrusions. Here, the variational autoencoder, Convolutional Neural Network (CNN), and extreme learning are integrated into a cascaded form to develop an A‐ASCENet model. Here, the parameters are optimized using the Revised Fitness‐based Lyrebird Optimization Algorithm (RF‐ILOA) from A‐ASCENet to enhance the performance of cybersecurity. At last, various WSN attacks like gray, scheduling, flooding, black holes, and holes are effectively detected. The performance of cybersecurity in WSN is compared over different traditional methods with some performance metrics.
Sivasankar et al. (Thu,) studied this question.