期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：2
DOI：10.14569/IJACSA.2022.0130275
语种：English
出版社：Science and Information Society (SAI)
摘要：In this study, an Intrusion Detection System (IDS) is designed based on Machine Learning classifiers, and its performance is evaluated for the set of attacks entailed in the UNSW- NB15 dataset. UNSW- NB15 dataset contains 2,540,226 realistic network data instances and 49 features. Most research uses a representative sample of this dataset with present training and testing subsets, which includes 257,673 records in total. The dataset was submitted to visual data analysis to discover poten-tial reasons or flaws which likely challenge Machine Learning classifiers. Pre-processing strategies are necessary before this data can be used for data-driven prototype development for IDS because of the class representation imbalance with pattern counts and feature overlap. The method used for pre-processing is implemented by min-max scaling in the normalization phase, followed by applying Elastic Net and Sequential Feature Selection (SFS) algorithms. This work employed ensemble methods using three base classifiers, namely Balanced Bagging, XGBoost, and RF-HDDT, augmented to address the imbalance issue. Parameters of Balanced Bagging and XGBoost are tuned for the imbalanced data, and the Hellinger distance metric supplements random Forest to address the limitations of the default distance metric. Two new algorithms are proposed to address the class overlap issue in the dataset and applied during training. These two algorithms are leveraged to help improve the performance on the testing dataset by affecting the final classification decision made by three base classifiers as part of the ensemble classifier, which employs a majority vote combiner. The performance evaluation of the proposed method for binary and multi-category classification was evaluated using standard metrics, including those generated from the confusion matrix, and compared to other studies using the same dataset. The proposed design outperforms those reported in the literature by a significant margin for binary and multi-category classification cases.
