Towards Efficient Features Dimensionality Reduction for Network Intrusion Detection on Highly Imbalanced Traffic
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Authors
Abdulhammed, Razan
Musafer, Hassan
Faezipour, Miad
Abuzneid, Abdelshakour A.
Issue Date
2019-03-29
Type
Other
Language
en_US
Keywords
CICIDS2017 , Intrusion detection system , Network intrusion detection
Alternative Title
Abstract
The performance of an IDS is significantly improved when the features are more discriminative and representative. This research effort is able to reduce the CICIDS2017 dataset’s feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6% in multi-class and binary classification. Furthermore, we propose a Multi-Class Combined performance metric CombinedMc with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS 2017 network intrusion dataset.