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DDoS Detection in IoT Devices using Machine Learning Clustering
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2021 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

With the exponential growth of IoT devices, there comes an increasing demand for security. The threats to IoT devices vary from eavesdropping to flooding (DoS or DDoS) attacks. To detect network-based anomalies, various machine learning algorithms can be used. The focus of this research is to evaluate the performance of machine learning clustering to distinguish between DDoS attacks and normal network traffic using an IoT-specific dataset. Optimizing the performance of the clustering algorithm with appropriate feature selection is considered in this thesis. The algorithm used in this research is K-Means clustering which is applied for an IoT dataset containing Mirai botnet – Distributed Denial-of-Service (DDoS) attack. The average accuracy achieved to differentiate DDoS attacks from normal network traffic entries using K-means clustering is 88.5% with an average precision of 85.5%.

Place, publisher, year, edition, pages
2021. , p. 38
Keywords [en]
IoT, Machine Learning, Clustering, K-means, DDoS
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-93787OAI: oai:DiVA.org:ltu-93787DiVA, id: diva2:1707966
Subject / course
Student thesis, at least 30 credits
Educational program
Information Security, master's level (120 credits)
Supervisors
Examiners
Available from: 2022-11-17 Created: 2022-11-02 Last updated: 2024-03-26Bibliographically approved

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Srinivas, Sri Krishna
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