TELKOMNIKA Telecommunication Computing Electronics and Control
Intrusion detection system for imbalance ratio class using weighted XGBoost classifier

TELKOMNIKA Telecommunication Computing Electronics and Control
Intrusion detection system for imbalance ratio class using weighted XGBoost classifier

Imbalanced ratio class
Intrusion detection
Weighted XGBoost

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.

Januar Al Amien, Hadhrami Ab Ghani, Nurul Izrin Md Saleh, Edi Ismanto, Rahmad Gunawan

http://telkomnika.uad.ac.id

Feb 16, 2023

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