期刊名称：Journal of Computer Networks and Communications
印刷版ISSN：2090-7141
电子版ISSN：2090-715X
出版年度：2021
卷号：2021
页码：1-9
DOI：10.1155/2021/8845540
出版社：Hindawi Publishing Corporation
摘要：This paper introduces a new integrated learning approach towards developing a new network intrusion detection model that is scalable and adaptive nature of learning. The approach can improve the existing trends and difficulties in intrusion detection. An integrated approach of machine learning with knowledge-based system is proposed for intrusion detection. While machine learning algorithm is used to construct a classifier model, knowledge-based system makes the model scalable and adaptive. It is empirically tested with NSL-KDD dataset of 40,558 total instances, by using ten-fold cross validation. Experimental result shows that 99.91% performance is registered after connection. Interestingly, significant knowledge rich learning for intrusion detection differs as a fundamental feature of intrusion detection and prevention techniques. Therefore, security experts are recommended to integrate intrusion detection in their network and computer systems, not only for well-being of their computer systems but also for the sake of improving their working process.
