Robust and energy-aware detection of Mirai botnet for future 6G-enabled IoT networks

Next-generation IoT wireless communication systems emphasise the importance and urgent need for energy-efficient security measures, thus requiring a balanced approach to address growing security vulnerabilities and fulfil energy demands in advanced wireless communication networks. However, the evolution of 6G networks and their integration with advanced technologies will revolutionise the IoT ecosystem while simultaneously introducing new security threats such as the Mirai malware, which targets IoT devices, infects multiple nodes, and depletes computational and energy resources. This study introduces a novel security algorithm designed to minimise energy consumption while effectively detecting botnet attacks at the smart device level. This research examines four distinct types of Mirai botnet attacks: scan, UDP, TCP, and ACK flooding.The experimental evaluation was conducted using real IoT device data collected from a Raspberry Pi setup combined with network traffic traces simulating the four Mirai attack scenarios to ensure realistic and reproducible results. Two ML algorithms, SVM and KNN, are employed to detect these botnet attacks, with each algorithm’s detection accuracy and energy efficiency thoroughly assessed. Results indicate that the proposed approach significantly enhances smart device security while minimising energy use. Findings show that the KNN algorithm outperforms SVM in terms of accuracy and energy efficiency for detecting Mirai botnet attacks, achieving detection rates above 99% across various attack types. This study highlights the importance of selecting suitable security techniques for IoT networks to address the evolving threats and energy demands of 6G-enabled wireless communication systems, providing valuable insights for future research.