AM-DRCN: Adaptive Migrating Bird Optimization-based Drift-Enabled Convolutional Neural Network for Threat Detection in Internet of Things

The Internet of Things (IoT) is a network that connects devices and sensors which enables the interconnection of people to everything and everywhere. The high network traffic has led to various cyber attacks, so efficient detection is needed. However, the existing methods pose drawbacks such as high complexity, latency, and computational issues. To overcome the limitations, an Adaptive Migrating bird optimization-based Drift-enabled convolutional neural network (AM-DrCN) classifier is proposed for effective threat detection. The Adaptive Migrating bird optimization (AMO) algorithm is employed to tune the hyperparameters of the model. In this research, the K Nearest neighbor (KNN) technique performed missing data imputation for estimating the missing details of unevenly distributed data, which enhances the overall efficiency of the model in threat recognition. Furthermore, the framework achieved improvements in the precision of 94.93%, accuracy of 94.75%, F1 score of 94.75%, and recall of 94.57% at 90 TP.