An Enhanced Intrusion Detection Classification Approach for Securing IoT Networks - Alharbi, Fayez (Communications in Mathematics and Applications, 2025)
The research investigates different classification methods that IDS developers use for developing intrusion detection systems. Recent rapid growth of internet of things (IoT) devices created a massive surge in available data requiring highly effective methods for preventing malicious activities. The research attempts to boost IDS effectiveness through ML algorithm implementation to obtain precise intrusion classification and identification. The research group obtained assessment data through three datasets which included IoT-Modbus and IoT-Fridge and IoT-Weather to measure classification frameworks’ abilities when detecting different threats affecting IoT systems. For the IoTFridge dataset, the one-vs-one (OvO) classification reached 100% accuracy; for the IoT-Weather and IoT-Modbus, the accuracy was 99.7% and 77.62%, respectively. The one-vs-rest (OvR) classification method yielded accuracies of 100% for IoT-Fridge data, 98.02% for IoT-Weather, and 77.62% for IoT-Modbus. The performance results on IoT-Modbus and IoT-Fridge datasets were comparable between OvO and OvR methods while OvO produced slightly superior results on the IoT-Weather dataset. The research demonstrates that multi-class classification techniques demonstrate outstanding performance for IDS systems which can boost IoT application cybersecurity capabilities. The major objective of this research is to introduce a new IDS system with enhanced potential of detecting threats in IoT environments, while handling specific IoT protection obstacles.
Keywords: Intrusion detection; Internet of Things; Classification; Cybersecurity; Data breaches