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Machine Learning in Spark for Attack Traffic Classification in IoT Devices Using Protocol Usage Statistics
Book chapter   Peer reviewed

Machine Learning in Spark for Attack Traffic Classification in IoT Devices Using Protocol Usage Statistics

Xiaojian Wang, Sikha Bagui and Subhash Bagui
Proceedings of International Conference on Innovations in Information and Communication Technologies. ICI2CT 2020. Algorithms for Intelligent Systems, pp.1-11
Algorithms for Intelligent Systems, Springer Singapore
05/13/2021
DOI: https://doi.org/10.1007/978-981-16-0873-5_1

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Abstract

In this paper, we use three different machine learning classifiers in spark, decision tree, random forest, and logistic regression, to classify attack traffic of different types of IoT devices from the Kitsune dataset. Kitsune allows us to use real-time network traffic information from data streams to dynamically generate features in real time. In this work, only protocol usage statistics generated from pcap files of the original data streams is used to detect malicious traffic in real time using the Big Data framework. Performance is measured in terms of accuracy, attack detection rate (ADR), false alarm rate (FAR), and runtime.

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