Abstract
Malicious attacks are common among the Internet of Things (IoT) devices that are installed in several locations like offices, homes, healthcare facilities, and transportation. Due to massive amounts of data created by IoT devices, machine learning is frequently used to detect cyber-attacks on these devices. The fact that fog devices may not have the computing or memory capability to identify threats in a timely manner is a source of concern. According to this article, machine learning model selection and real-time prediction can both be offloaded from the cloud, and both jobs can be performed by fog nodes, which are distributed computing devices. A cloud-based ensemble machine learning model is constructed using the provided method, and subsequently, attacks on fog nodes are identified in real time using the model. The NSL-KDD dataset is used to evaluate the performance of this approach. The results indicate that the proposed technique is effective in terms of a variety of performance criteria, including execution time, precision, recall, accuracy, and the F1 measure.
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Sivakamasundari, S., Dharmarajan, K. (2023). Certain Investigations on Ensemble Learning and Machine Learning Techniques with IoT in Secured Cloud Service Provisioning. In: Khanna, A., Polkowski, Z., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes in Networks and Systems, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-19-7615-5_53
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