• Vijay Kumar Khatri
• Dr. Vandana Roy

This study focuses on Machine Learning techniques for Internet of Things security threats detection. It seeks to investigate the feasible of using auto-encoders to detect IoT botnets. Botnets can develop Distributed Denial-of-Service (DDoS) attacks and present a major security concern in IoT networks, as there is no single method has demonstrated the potential to address this security threat. These methods often fail to meet Internet of Things (IoT) environments requirements, such as processing power and energy consumption. Auto-encoders offers one of the solutions to botnet detection. Future research needs to explore the opportunities that auto-encoders present in the detection of IoT botnets.

Machine Learning Detection, IoT, Botnets, DDoS

"IoT Botnets anomalies detection using machine learning auto-encoders ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.c652-c656, May-2023, Available :http://www.jetir.org/papers/JETIR2305296.pdf

"IoT Botnets anomalies detection using machine learning auto-encoders ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppc652-c656, May-2023, Available at : http://www.jetir.org/papers/JETIR2305296.pdf