Ensemble Learning with Sleep Mode Management to Enhance Anomaly Detection in IoT Environment

-The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need for energy-efficient cybersecurity measures. This presents the dual challenge of maintaining robust security while minimizing power consumption. Thus, this paper proposes enhancing the machine learning performance through Ensemble Techniques with Sleep Mode Management (ELSM) approach for IoT Intrusion Detection Systems (IDS). The main challenge lies in the high- power consumption attributed to continuous monitoring in traditional IDS setups. ELSM addresses this challenge by introducing a sophisticated sleep-awake mechanism, activating the IDS system only during anomaly detection events, effectively minimizing energy expenditure during periods of normal network operation. By strategically managing the sleep modes of IoT devices, ELSM significantly conserves energy without compromising security vigilance. Moreover, achieving high detection accuracy with limited computational resources poses another problem in IoT security. To overcome this challenge, ELSM employs ensemble learning techniques with a novel voting mechanism. This mechanism integrates the outputs of six different anomaly detection algorithms, using their collective intelligence to enhance prediction accuracy and overall system performance. By combining the strengths of multiple algorithms, ELSM adapts dynamically to evolving threat landscapes and diverse IoT environments. The efficacy of the proposed ELSM model is rigorously evaluated using the IoT Botnets Attack Detection Dataset, a benchmark dataset representing real-world IoT security scenarios, where it achieves an impressive 99.97% accuracy in detecting intrusions while efficiently managing power consumption.