1. Intrusion detection system for imbalance ratio class using weighted XGBoost classifier.
- Author
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Amien, Januar Al, Ghani, Hadhrami Ab, Md Saleh, Nurul Izrin, Ismanto, Edi, and Gunawan, Rahmad
- Subjects
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INTERNET of things , *BIG data , *ACTIVITIES of daily living - Abstract
The rapid development of the internet of things (IoT) has taken an important role in daily activities. As it develops, IoT is very vulnerable to attacks and creates IoT for users. Intrusion detection system (IDS) can work efficiently and look for activity in the network. Many data sets have already been collected, however, when dealing with problems involving big data and hight data imbalances. This article proposes, using the dataset used by BotIoT to evaluate the system framework to be created, the XGBoost model to improve the detection performance of all types of attacks, to control unbalanced data using the imbalance ratio of each class weight (CW). The experimental results show that the proposed approach greatly increases the detection rate for infrequent disturbances. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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