Enhancement performance of random forest algorithm via one hot encoding for IoT IDS
Article
Hussein, A. Y., Falcarin, P. and Sadiq, A. T. 2021. Enhancement performance of random forest algorithm via one hot encoding for IoT IDS. Periodicals of Engineering and Natural Sciences. 9 (3), pp. 579-591. https://doi.org/10.21533/pen.v9i3.2204
Authors | Hussein, A. Y., Falcarin, P. and Sadiq, A. T. |
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Abstract | The random forest algorithm is one of important supervised machine learning (ML) algorithms. In the present paper, the accuracy of the results of the random forest (RF) algorithm has been improved by the use of the One Hot Encoding method. The Intrusion Detection System (IDS) can be defined as a system that can predict security vulnerabilities within network traffic and is located out of range on a network infrastructure. It does not affect the efficiency of the built-in network because it analyzes a copy of the built-in traffic flow and reports results to the administrator by giving alerts. However, since IDS is a listening system only, it cannot take automatic action to prevent an attack or security vulnerability detected from infecting the system, it provides information about the source address to start the break-in, the address of the target and the type of suspected attack. The IoTID20 dataset is used to verify the improved algorithm, where this dataset is having three targets, the proposed system is compared with the state-of-art approaches and shows superiority over them. |
Journal | Periodicals of Engineering and Natural Sciences |
Journal citation | 9 (3), pp. 579-591 |
ISSN | 2303-4521 |
Year | 2021 |
Publisher | International University of Sarajevo |
Publisher's version | License File Access Level Anyone |
Digital Object Identifier (DOI) | https://doi.org/10.21533/pen.v9i3.2204 |
Publication dates | |
Online | Aug 2021 |
Publication process dates | |
Deposited | 19 Aug 2021 |
Copyright holder | © 2021 The Authors |
https://repository.uel.ac.uk/item/89q06
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