UIoTN‐PMSE: Ubiquitous IoT network‐based predictive modeling in smart environment

Summary We proposed a three‐stage intrusion detection system that utilizes a predictive machine learning model to identify and mitigate attacks on ubiquitous network. In the first stage, we applied Apriori‐enabled Association Rule Mining (AARM) feature selection with support vector machine (SVM) for classification of flow of network. Second, we proposed ensemble learning‐based AARM model (PAEL) for behavior analysis. Finally, for classification of multi‐task labels, we proposed swarm bat optimization‐based PAEL model. The trained model is applied to edge and fog computing devices to obtain lower resource utilization and improve the efficiency of the system. The intrusion detection process is performed in three stages: (i) at the edge devices, where abnormal data from network traffic from IoT devices were identified, (ii) the abnormal data sample is sent to fog computing deivce to confirm the attacks and abnormalities, (iii) final identified data sample is sent to cloud server. At cloud, proposed predictive machine learning (ML)‐based generalized weight sum‐enabled ensemble learning (PML‐GWEL) model is trained on sample data, including new detected samples, to continually improve its accuracy. Once the model is trained, it is published to all nodes in the network to update their primary detector models and clear out any outdated pre‐detector models. This process helps to reduce the hardware resources used by the pre‐detector models and improve the overall efficiency of the system. The proposed model is compared with other existing techniques. This proposed architecture is a three‐stage predictive machine learning enable intrusion detection (three‐stage PML‐ID) system for ubiquitous infrastructure including smart city, smart healthcare, smart transportation, smart farming, smart grid, and smart manufacturing.