Abstract
Increasing attacks in the internet domain has led to the need of intrusion detection system (IDS) and many researchers have trying to improve the performance of IDS. Network-based IDS tries to detect intrusion using network-based features. KDD 1999 dataset has been widely used as benchmark intrusion detection dataset. It has 41 features. This work tries to extract a subset of 41 features without degrading the performance of IDS. For dimensionality reduction, this work uses Information gain(IG), Gain ratio(GR) and Correlation-based feature selection algorithms. This work also proposes a heuristic based dimensionality reduction approach to further improve the performance of the aforementioned feature selection algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
