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2020 | OriginalPaper | Buchkapitel

A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks

verfasst von : Imtiaz Ullah, Qusay H. Mahmoud

Erschienen in: Advances in Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

The exponential growth of the Internet of Things (IoT) devices provides a large attack surface for intruders to launch more destructive cyber-attacks. The intruder aimed to exhaust the target IoT network resources with malicious activity. New techniques and detection algorithms required a well-designed dataset for IoT networks. Firstly, we reviewed the weaknesses of various intrusion detection datasets. Secondly, we proposed a new dataset namely IoTID20 generated dataset from [1]. Thirdly we provide a significant set of features with their corresponding weights. Finally, we propose a new detection classification methodology using the generated dataset. Our proposed IoT botnet dataset will provide a reference point to identify anomalous activity across the IoT networks. The IoT Botnet dataset can be accessed from [2]. The new IoTID20 dataset will provide a foundation for the development of new intrusion detection techniques in IoT networks.

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Titel: A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks
verfasst von: Imtiaz Ullah
Qusay H. Mahmoud
Verlag: Springer International Publishing
Buch: Advances in Artificial Intelligence
Print ISBN: 978-3-030-47357-0

Electronic ISBN: 978-3-030-47358-7

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-47358-7_52

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