International Journal of Computing and Mathematics

Wireless Sensor Networks (WSNs) have been a crucial IoT development and, while strong advantages, security problems remain. New cyberattacks are growing as more computers are linked to the internet, following well-known attacks that represent serious risks to the security, credibility, and efficiency of data in WSNs. For many software and scientific questions, the implementation of intelligent computing works effectively; but the defense systems focused on computational intelligence (CI) are not being adequately examined. In this article, it examined two WSN intrusion detection evolutionary computing strategies. A neural network with backpropagation was connected with a classifier of the support vector machine. Detection rates reached by the two methodologies for cyber attacks were identified using the ADFA-LD and ADFA-WD dataset. The study shows that both approaches give a high true positive rate and a low false-positive rate, making them both good intrusion detection solutions. In particular, by illustrating its responsibility to sustain low data sets, thus retaining a reasonable FPR rate below the appropriate threshold, it also demonstrates the suitability of support vector machine classification models for anomaly detection.