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Book chapter
Machine Learning in Spark for Attack Traffic Classification in IoT Devices Using Protocol Usage Statistics
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
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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Details
- Title
- Machine Learning in Spark for Attack Traffic Classification in IoT Devices Using Protocol Usage Statistics
- Publication Details
- Proceedings of International Conference on Innovations in Information and Communication Technologies. ICI2CT 2020. Algorithms for Intelligent Systems, pp.1-11
- Resource Type
- Book chapter
- Publisher
- Springer Singapore; Singapore
- Series
- Algorithms for Intelligent Systems
- Copyright
- © 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Identifiers
- 9789811608728; 99380179096806600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Computer Science; Mathematics and Statistics
- Language
- English