Abstract:
End-user Internet of Things (IoT) devices, including security cameras, smart appliances, home monitors, and thermostats, are becoming more prevalent in households. Additi...Show MoreMetadata
Abstract:
End-user Internet of Things (IoT) devices, including security cameras, smart appliances, home monitors, and thermostats, are becoming more prevalent in households. Additionally, the proliferation of devices facilitates the propagation of security concerns like DoS and spoofing. However, it is difficult for conventional rule-based security systems to recognize IoT assaults due to the development of heterogenous devices in the IoT ecosystem. Artificial Intelligence (AI) techniques can be a solution which enables the creation of an effective security model based on actual data from each device. In this work, IoT botnets are detected and classified using machine learning (ML) and deep learning (DL) based algorithms. Six ML models and three DL models are used to assess the system's performance. The best-performing model is also implemented as an API.
Published in: 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)
Date of Conference: 15-16 December 2022
Date Added to IEEE Xplore: 02 January 2023
ISBN Information: