Please use this identifier to cite or link to this item:
http://repository.futminna.edu.ng:8080/jspui/handle/123456789/14363
Title: | A Multi-Step Adaptive Synthetic Oversampling and Random Forest Cascaded Model for Multi-Class Intrusion Detection |
Authors: | Isah, H.A Abisoye, Opeyemi Aderiike Lawal, K. |
Keywords: | wireless sensor network, instruction detection data transfer |
Issue Date: | 22-Jun-2021 |
Publisher: | Proceedings of the 2021 Sustainable Engineering and Industrial Technology Conference |
Series/Report no.: | Faculty of Engineering, UNN, 2021; |
Abstract: | Hackers have developed better and smart traditions to attack WSN in sequence when data are transfer in systems. The harm, hackers can carry out upon thorough a WSNs is well understood. A reasonable damage scenario can be envisaged where a state intercepting encrypted financial data gets hacked. Logical cyber security systems have become without doubt significantfor improved security against malicious threats. The proposed multi-step adaptive synthetic oversampling and random forest cascaded model for intrusion detection system (IDS) using big data, The NSL-KDD dataset used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed IDS system, performance better compared to existing methods. |
URI: | http://repository.futminna.edu.ng:8080/jspui/handle/123456789/14363 |
Appears in Collections: | Computer Science |
Files in This Item:
File | Description | Size | Format | |
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A Multi-Step ynthetic.pdf | 532.79 kB | Adobe PDF | View/Open |
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