| Abstrak/Abstract |
The application of Machine Learning (ML)-based
Intrusion Detection System (IDS) has been widely used. The
advantage of ML-based IDS is that it can detect intrusions in
the network. However, in its application, there are still false
positive detections on the IDS. False positive detection occurs
due to improper ML techniques. This research applies an S-
SDN model based on Ensemble Learning (EL) to overcome this
problem. The S-SDN model is built from three base-learners,
namely SVM, Decision Tree, and Naïve Bayes with the Stacking
technique. Furthermore, the S-SDN model is used as a classifier
on the IDS to detect intrusions. S-SDN was validated using
the UNSW-NB15 dataset. Based on the experiment, S-SDN’s
performance was superior to the old method based on a single
classifier. The performance of S-SDN can achieve an accuracy
of 83.19%. In comparison, the old method based on a single
classifier (SVM) can only achieve an accuracy of 75.89%, and
the ensemble classifier (Bagging-DT) is only 80,09%. As for
further research, the development of EL-based IDS still needs
to be improved. For example, it builds an EL-based model with
feature selection techniques and different base learners. |
