A multi-level random forest model-based intrusion detection using fuzzy inference system for Internet of Things networks

Intrusion detection (ID) methods are security frameworks designed to safeguard network information systems. The strength of an intrusion detection method is dependent on the robustness of the feature selection method. This study developed a multilevel random forest algorithm for intrusion detection using a fuzzy inference system. The strengths of the flter and wrapper approaches are combined in this work to create a more advanced multi-level feature selection technique, which strengthens network security. The frst stage of the multi-level feature selection is the flter method using a correlation-based feature selection to select essential features based on the multi-collinearity in the data. The correlation-based feature selection used a genetic search method to choose the best features from the feature set. The genetic search algorithm assesses the merits of each attribute, which then delivers the characteristics with the highest ftness values for selection. A rule assessment has also been used to determine whether two feature subsets have the same ftness value, which ultimately returns the feature subset with the fewest features. The second stage is a wrapper method based on the sequential forward selection method to further select top features based on the accuracy of the baseline classifer. The selected top features serve as input into the random forest algorithm for detecting intrusions. Finally, fuzzy logic was used to classify intrusions as either normal, low, medium, or high to reduce misclassifcation. When the developed intrusion method was compared to other existing models using the same dataset, the results revealed a higher accuracy, precision, sensitivity, specifcity, and F1-score of 99.46%, 99.46%, 99.46%, 93.86%, and 99.46%, respectively. The classifcation of attacks using the fuzzy inference system also indicates that the developed method can correctly classify attacks with reduced misclassifcation. The use of a multi-level feature selection method to leverage the advantages of flter and wrapper feature selection methods and fuzzy logic for intrusion classifcation makes this study unique