COMPARATIVE ANALYSIS OF RANDOM FOREST AND ADABOOST LEARNING MODELS FOR THE CLASSIFICATION OF ATTACKS IN INTERNET OF THINGS

Abstract

Attacks are actions that attempt to break one of the following properties of the computer system: confidentiality, integrity, and availability. The immense increment in the amount of internet applications and the appearance of modern networks has created the need for improved security mechanisms. Internet of Things (IoT) is a system that uses the Internet to facilitate communication between sensors and devices. Several approaches have been used to build attacks detection system in the past. This study built two ensemble models for the classification of attacks using Random Forest and Adaboost algorithms respectively. Feature importance was used for selecting promising attributes from the IoT intrusion dataset. Thereafter, the results of the classification models were evaluated and compared. The models were evaluated based on when feature selection technique was applied and without respectively. For Random Forest-based classification model with feature selection, 99.0% ,0.95,0.88,0.82, were obtained for accuracy, recall, f1-score, and precision respectively while without feature selection 69.0%,0.86,0.76,0.64 were obtained respectively. For Adaboost-based classification model with feature selection 99.0%.0.69,0.61,0.66 were obtained for accuracy, recall, f1-score and precision respectively. Without feature selection the Adaboost model recorded 58.0%,0.58,0.48,0.50 respectively. The results showed that both models achieved high rates with feature selection technique used, with Random Forest performing slightly better, both learning models showed promised performances in classifying attacks in IoT environments. This study concluded that the use of the chosen feature selection method helped improve the performances of the two ensembles in the classification of attacks in the IoT dataset.