Intrusion detection for the internet of things (IoT) based on the emperor penguin colony optimization algorithm

In the Internet of Things (IoT), the data that are sent via devices are sometimes unrelated, duplicated, or erroneous, which makes it difficult to perform the required tasks. Hence transmitted data need to be filtered and selected to suit the nature of the problem being dealt with in order to achieve the highest possible level of security. Feature selection is the process of identifying the suitable characteristics needed from a dataset's whole data set for usage in a certain task (FS). This study proposes a novel wrapper FS model that uses the emperor penguin colony (EPC) method to explore the issue space and a K-nearest neighbor classifier to solve FS for IoT challenges. In experiments, the proposed EPC model was applied to nine well-known IoT datasets in order to evaluate its performance. The results showed that the model had clear superiority over the multi-objective particle swarm optimization (MOPSO) and MOPSO-Lévy methods in terms of accuracy and FS size, achieving 98% classification accuracy. The results also provided a clear understanding of the effect of the EPC algorithm on various filter methods, including the ReliefF, correlation, information gain and symmetrical methods.