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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Nurul Izrin Md Saleh
University Malaysia Kelantan
Computer Science & IT

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Intrusion detection system for imbalance ratio class using weighted XGBoost classifier Januar Al Amien; Hadhrami Ab Ghani; Nurul Izrin Md Saleh; Edi Ismanto; Rahmad Gunawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 5: October 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i5.24735

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.
Co-Authors Edi Ismanto Gunawan, Rahmad Hadhrami Ab Ghani Januar Al Amien