Comparative Study of Machine Learning Algorithms for IoT Security
Ishmeet Jaggi, Heena Wadhwa · 2023
Our daily lives are a big part of how the Internet of Things (IoT) has had a great impact in numerous industries thanks to technological advancements for resource optimization. IoT devices primarily rely on gathering data from the physical environment and sending it via a network of heterogeneous devices to perform services. These devices frequently have sensitive data on them. It is, therefore, crucial to guarantee the network is safe and secure because typical security procedures do not apply to Internet of Things (IoT) devices. This research discusses various security algorithms that developed recently and the importance of having well-built datasets with numerous features that simulate real-life situations to verify the proposed models. This paper consists of a comparison between current Machine learning (ML) and Deep learning (DL) algorithms for security systems. In this paper, various MLP models have been envaulted using the CICIDS2017 dataset and we have also proposed a modified version of the KNN classifier in order to improve the existing KNN classifier.