Low-rate distributed denial of service attacks detection insoftware defined network-enabled internet of things usingmachine learning combined with feature importance

abizar, muhammad and Syahputra, Muhammad Ferry Septian Ihzanor and Habibullah, Ahmad Rizky and Aditya, Christian Sri Kusuma and Sumadi, Fauzi Dwi Setiawan (2023) Low-rate distributed denial of service attacks detection insoftware defined network-enabled internet of things usingmachine learning combined with feature importance. IAES International Journal of Artificial Intelligence (IJ-AI), 12 (4). pp. 1974-1984. ISSN 2089-4872 / 2252-8938

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Abstract

One of the main challenges in developing the internet of things (IoT) is theexistence of availability problems originated from the low-rate distributeddenial of service attacks (LRDDoS). The complexity of IoT makes theLRDDoS hard to detect because the attack flow is performed similarly to theregular traffic. Integration of software defined IoT (SDN-Enabled IoT) isconsidered an alternative solution for overcoming the specified problemthrough a single detection point using machine learning approaches. Thecontroller has a resource limitation for implementing the classificationprocess. Therefore, this paper extends the usage of Feature Importance toreduce the data complexity during the model generation process and choosean appropriate feature for generating an efficient classification model. Theresearch results show that the Gaussian Naïve Bayes (GNB) produced themost effective outcome. GNB performed better than the other algorithmsbecause the feature reduction only selected the independent feature, which hadno relation to the other features.

Item Type: Article
Keywords: Feature importance; Internet of things; Low-rate distributed denial of service; Machine learning; Software defined network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering > Department of Informatics (55201)
Depositing User: christianskaditya Christian Sri Kusuma Aditya, S.Kom., M.Kom
Date Deposited: 22 Apr 2024 04:56
Last Modified: 24 Jul 2024 07:52
URI: https://eprints.umm.ac.id/id/eprint/5648

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