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.2022.0130276
语种：English
出版社：Science and Information Society (SAI)
摘要：The intrusion detection system has been widely studied and deployed by researchers for providing better security to computer networks. The increasing volume of attacks, com-bined with the rapid improvement of machine learning (ML) has made the collaboration of intrusion detection techniques with machine learning and deep learnings are a popular subject and a feasible approach for cyber threat protection. Machine learning usually involves the training process using huge sample data. Since the huge input data may cause a negative effect on the training and detection performance of the machine learning model, feature selection becomes a crucial technique to rule out the irrelevant and redundant features from the dataset. This study applied a feature selection approach for intrusion detection that incorporated state-of-the-art feature selection algorithms with attack characteristic feature to produce an optimized set of features for the machine learning algorithms, which was then used to train the machine learning model. CSECIC- IDS2018 dataset, the most recent benchmark dataset with a wide attack diversity and features have been used to create the efficient feature subset. The result of the experiment was produced using machine learning models with a decision tree classifier and analyzed with respect to the accuracy, precision, recall, and f1 score.
