Enhancing training time and sustainability in Intrusion Detection Systems on Machine Learning

The increasing complexity and volume of network traffic in the Internet of Things (IoT) environments, coupled with the rapid evolution of cyber threats, has rendered traditional Intrusion Detection Systems (IDS) less effective. In response, there is an urgent need to develop a more efficient IDS that can not only detect a wider range of attacks but also adapt quickly to new, previously unknown threats. This study addresses the issues of prolonged training times and high computational resource consumption in IDS, with a particular focus on achieving sustainability without compromising performance. We put forth a solution to reduce the intrusion detection pipeline, with an emphasis on training time, that employs a novel machine learning (ML) model, PerpetualBooster, which has not previously been utilized in cybersecurity. This model is designed to minimize training times and computational resource consumption while maintaining high detection performance. The CIC Modbus dataset was used to evaluate the performance of our approach. PerpetualBooster was trained in a few seconds, demonstrating a reduction in training time compared to other ML algorithms. These results illustrate the potential of the proposed model as a sustainable, high-performance solution for real-time and energy-efficient IDS in IoT environments, addressing critical challenges in both cybersecurity and environmental sustainability.