Neglected Cyber Threats on the Rise: Combatting IoT Botnet Infection Techniques through the Elucidation of Machine Learning
Eric Blancaflor, Renee Charlene B. Evangelista, Rondisney R. Maligmat, Denise Nicole B. Marcelo, Bryan Jorel P. Pablo, Mark Joshua B. Vilar · 2023
Internet of Things (IoT) devices have become prevalent in this age. The overwhelming amount of neglected IoT devices has become a powerful platform for enforcing botnets and launching DDoS attacks. Several IoT botnets have been developed and are constantly evolving due to increased security measures placed by information security professionals - some of which are: Bashlite, Mirai, and Torii. Machine learning is emerging as an effective tool for botnet detection - distinguishing malicious traffic in the network from normal traffic. Although numerous studies have already been conducted about botnet detection using machine learning algorithms, more is needed to explore machine learning models explicitly catering to each type of IoT device and the variation of botnets. Hence, this study will serve as an empirical basis for using machine learning algorithms to identify and counteract IoT botnets, underscoring the vital role that proactive security measures play in protecting interconnected networks and neglected devices. Four IT experts with different extensive job experiences and expertise were interviewed to collect valuable insights and data on the topic.