Abstract
Cyberattacks have increased as a consequence of the expansion of the Internet of Things (IoT). It is necessary to detect anomalies so that smart devices need to be protected from these attacks, which must be mitigated at the edge of the IoT network. Therefore, efficient detection depends on the selection of an optimal IoT traffic feature set and the learning algorithm that classifies the IoT traffic. There is a flaw in the existing anomaly detection systems because the feature selection algorithms do not identify the most appropriate set of features. In this article, a layered paddy crop optimization (LPCO) algorithm is suggested to choose the optimal set of features. Furthermore, the use of smart devices generates tremendous traffic, which can be labelled as either normal or attack using a capsule network (CN) approach. Five network traffic benchmark datasets are utilized to evaluate the proposed approach, including NSL KDD, UNSW NB, CICIDS, CSE-CIC-IDS, and UNSW Bot-IoT. Based on the experiments, the presented approach yields assuring results in comparison with the existing base classifiers and feature selection approaches. Comparatively, the proposed strategy performs better than the current state-of-the-art approaches.