Performance Analysis of Ensemble Learning for Botnet Detection in IoT

The phenomenal rise of the Internet of Things (IoT) has connected a wide range of devices, from industrial systems to household appliances, revolutionizing daily life, yet this unprecedented growth has introduced significant cybersecurity challenges. One of the most pressing issues is the emergence of BotNets, which are networks of compromised Internet of Things devices used by hostile actors to launch huge attacks like Distributed Denial of Service (DDoS). Because these BotNets exploit the often-weak security measures incorporated into Internet of Things devices, they represent a significant threat to global cybersecurity. This paper investigates the applicability of ensemble learning techniques, including Bagging, Boosting, and Stacking, for BotNet detection in Internet of Things contexts. Utilizing the BoTNeTIoT-L01 dataset, our comprehensive analysis reveals that both Bagging and Stacking, with their perfect accuracy rates of $\mathbf{1. 0 0}$ across all attack types, outperform the other methods, highlighting their potential as critical tools for enhancing IoT security. The implications of these findings are significant, providing a pathway for developing more robust cybersecurity strategies that can adapt to the evolving landscape of cyber threats. By focusing on advanced ensemble learning techniques, this research offers valuable insights into the future of IoT security, ensuring that these interconnected devices remain both innovative and secure.