Hybrid Rule based Classification of Attacks in Internet of Things (IoT) Intrusion Detection System

When dealing with any kind of network traffic, an intrusion detection system is the essential element of security. This also applies to the Internet of Things (IoT) network, where the diverse components involved and the wireless connection method make cyber-related attacks more novel. So, it is necessary to use principles that a human would use to recognize attacks in order to comprehend the attacks and develop a workable method of detecting and responding to intrusions. The hardest element of using machine learning models for intrusion detection is that they can't handle the newer sorts of assaults, thus humans have to help by classifying the traffic as either normal or an attack using rules. In order to give the machine learning model, the capacity to employ hard-coded rules whenever necessary and to make predictions in circumstances where it cannot be done, the goal of this work is to incorporate hard-coded rules into the machine learning model. The attacks are recognized by an intrusion detection system that uses a hybrid rule-based machine learning model. The IoTID20 dataset is used for the attack classification, and the hybrid methodology's overall accuracy was 93.17%.