Using Incremental Ensemble Learning Techniques to Design Portable Intrusion Detection for Computationally Constraint Systems

Abstract

Computers have evolved over the years, and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy, and industry to communicate with each other. This era is known as the Internet of Things (IoT). IoT has several benefits in a country’s economy’s health, energy, transportation, and agriculture sectors. These enormous benefits, coupled with the computational constraint of IoT devices, make it challenging to deploy enhanced security protocols on them, making IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. This study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding trees to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other proposed state-of-the-art methods and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98%. We also evaluated the memory consumption of our model, which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.