An intelligent IoT intrusion detection system using HeInit-WGAN and SSO-BNMCNN based multivariate feature analysis

Devices connected to the Internet of Things (IoT) are growing in popularity across multiple industries. IoT links intelligent homes, buildings, businesses, and communities, and also links computers and mobile devices. Cybersecurity in IoT networks remains challenging, although many intrusion detection services for assault avoidance systems have been developed. Because of enormous growth in computing resources, attack surfaces, communications infrastructure, and attack rates, IoT services encounter substantial security issues. Innovative intrusion-detection systems ensuring data security, availability, and integrity have been developed to mitigate security problems; these systems are recommended for preventing common internal and external assaults and managing IoT environments. To clearly understand any impending attacks, the data are first preprocessed. Missing values are categorized with the concordance connection coefficient, and preprocessing is applied before handling of the missing values. The unevenly distributed and overlapping data are then handled through maximum likelihood estimation-based robust scalar (MLE-RS) scaling followed by random oversampling-based cluster-initialized divisive clustering (ROS-CInitDC). This study conceptualizes a Squirrel search optimization-based Bengio Nesterov momentum convolutional neural network (SSO-BNMCNN)-based approach, which indicates the relationships among information by considering causation. It keeps an optimal processing time and correctness to get data understanding. The selected features are then tested against the categorization of a He initialization-based Wasserstein gradient penalty loss generative adversarial network (HeInit-WGAN). The HeInit-WGAN technique identifies attacks more reliably and accurately than existing methods, reducing false alarms. The proposed strategy had an F-measure of 0.91, and scores of 0.96 for precision, 0.91 for awareness, and 0.98 for explicitness. Moreover, it decreased the false acceptance rate, achieving a 0.02 false positive rate and 0.09 false negative rate.