Intrusion detection system using resampled dataset - a comparative study. (20th April 2023) - Patel, N.D.; Mehtre, B.M.; Wankar, Rajeev
Existing machine-learning research aims to improve the predictive capability of datasets using various feature selection and classification models. In the intrusion detection, data consists of normal data and a minimal number of attack data. This data imbalance causes prediction performance degradation due to factors such as prediction bias of small data presence of outliers. To address this issue, we oversampled the minority class of the existing intrusion detection datasets using four data oversampling methods and tested using three different classifiers. To further ensure the real-time applicability of these oversampling methods with these classifiers, we also generate a real-time testbed (RTT) resampled dataset. It is observed that CTGAN oversampling method, along with the LightGBM classifier, gives outperforming results on the existing CICIDS2018 and RTT resampled dataset. Test results also outperformed over the existing intrusion detection methods and datasets (credit card, gambling fraud, ISCX-Bot-2014, and CICIDS2017) in terms of accuracy, precision, etc.