Ensemble learning approach to enhancing binary classification in Intrusion Detection System for Internet of Things

Abstract

The Internet of Things (IoT) has experienced significant growth and plays a crucial role in daily activities. However, along with its development, IoT is very vulnerable to attacks and raises concerns for users. The Intrusion Detection System (IDS) operates efficiently to detect and identify suspicious activities within the network. The primary source of attacks originates from external sources, specifi-cally from the internet attempting to transmit data to the host network. IDS can identify unknown attacks from network traffic and has become one of the most effective network security. Classification is used to distinguish between normal class and attacks in binary classification problem. As a result, there is a rise in the false positive rates and a decrease in the detection accuracy during the model's training. Based on the test results using the ensemble technique with the ensemble learning XGBoost and LightGBM algorithm, it can be concluded that both binary classification problems can be solved. The results using these ensemble learning algorithms on the ToN IoT Dataset, where binary classification has been performed by combining multiple devices into one, have demonstrated improved accuracy. Moreover, this ensemble approach ensures a more even distribution of accuracy across each device, surpassing the findings of previous research.