Ioulianou, Philokypros P., Vassilakis, Vassilios G. orcid.org/0000-0003-4902-8226 and Shahandashti, Siamak F. orcid.org/0000-0002-5284-6847 (2022) ML-based Detection of Rank and Blackhole Attacks in RPL Networks. In: 13th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2022:Proceedings. 13th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2022, 20-22 Jul 2022 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2022 . IEEE , PRT , pp. 338-343.
Abstract
Although IoT security is a field studied extensively, recent attacks such as BotenaGo show that current security solutions cannot effectively stop the spread of IoT attacks. Machine Learning (ML) techniques are promising in improving protection against such attacks. In this work, three supervised ML algorithms are trained and evaluated for detecting rank and blackhole attacks in RPL-based IoT networks. Extensive simulations of the attacks are implemented to create a dataset and appropriate fields are identified for training the ML model. We use Google AutoML and Microsoft Azure ML platforms to train our model. Our evaluation results show that ML techniques can be effective in detecting rank and blackhole attacks, achieving a precision of 93.3%.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | Publisher Copyright: © 2022 IEEE. |
Keywords: | RPL security,blackhole attack,rank attack,intrusion detection,Machine learning,IoT security,Internet of Things (IoT) |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 01 Jul 2022 08:50 |
Last Modified: | 14 Mar 2025 00:14 |
Published Version: | https://doi.org/10.1109/CSNDSP54353.2022.9908049 |
Status: | Published |
Publisher: | IEEE |
Series Name: | 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2022 |
Identification Number: | 10.1109/CSNDSP54353.2022.9908049 |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188368 |