BKIDset - A New Intrusion Detection Dataset To Mitigate The Class Imbalance Problem

Anh T Nguyen, Le Huy Hoang, Vu Dinh Minh, Tran Hoang Hai

Abstract: Applying machine learning techniques to Intrusion Detection System (IDS) is one efficient method to detect existing and new types of network attacks with minimal human supervision and great accuracy. The classification performance of such systems depend heavily on the dataset they are trained on. Most currently deployed datasets suffer from a high imbalance between normal network traffic and anomaly network traffic, which limits the system’s ability to detect minority classes with high accuracy. In this paper, we propose a new model for generating and collecting anomaly network traffic, which is used in conjunction with CICIDS2017 [1] dataset to create a new dataset namely BKIDset, which mitigates the class imbalance problem encountered in CICIDS2017 dataset and introduces additional types of denial-of-service attacks. The paper also proposes different machine learning models trained on BKIDset, that achieve a high accuracy score of 99%.