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Title	Deep Learning Framework For Anomaly Detection In Iot Enabled Systems
ID_Doc	17891
Authors	Selvakumar B.; Sridhar Raj S.; Vijay Gokul S.; Lakshmanan B.
Year	2021
Published	Signals and Communication Technology
DOI	http://dx.doi.org/10.1007/978-981-16-6186-0_5
Abstract	In recent years, the rapid advancement in IoT-enabled systems emerges in various domains like healthcare monitoring systems, disaster management systems, environmental monitoring systems, and smart cities so on. However, it attracts the attackers to exploit and damage the IoT enabled systems. Since IoT based network systems use wireless communication for exchanging data, it is prone to various kinds of cyberattacks. The aforementioned problem can be alleviated by adopting a deep learning-based framework for detecting anomalies in IoT network systems. In this chapter, we discuss deep learning models like convolutional neural networks and its variant LeNet-5 to detect attacks like reconnaissance, denial of service (DoS), and information theft. The evaluation of the deep learning models is experimented on the BoT-IoT dataset and shows better performance. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Author Keywords	Anomaly detection; BoT-IoT dataset; Convolutional neural network; Deep learning; IoT security; LeNet-5

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