A simulation-driven computational framework for adaptive energy-efficient optimization in machine learning-based intrusion detection systems

This paper presents GreenMU, an innovative proposed novel framework designed to address the two main challenges: energy efficiency as one of the main research components and detection performance in intrusion detection systems. In the proposed research paper study, by integrating advanced machine learning techniques such as random forest classifier and support vector machines classifier with knowledge distillation and adaptive energy-aware optimization, GreenMU achieves a balanced trade-off between computational efficiency and cybersecurity accuracy. The proposed MUGuard algorithm is at the framework's core, which dynamically adjusts computational complexity based on real-time actual energy constraints and the evolving threat landscape. Extensive simulations conducted on the KDD 1999 dataset demonstrate that GreenMU achieves a detection accuracy close to 99%, significantly surpassing standard baseline models while reducing energy consumption by 31%. Furthermore, the framework improves computational efficiency, reducing processing time by 15% and making it highly effective for resource-constrained environments such as IoT and edge computing. This research paper study highlights the potential of green artificial intelligence in advancing cybersecurity, providing a scalable, sustainable, and high-performing solution to modern intrusion detection challenges.