A Network Intrusion Detection System: Enhanced Classification via Clustering Model
No Thumbnail Available
Date
2015-12-10
Journal Title
Journal ISSN
Volume Title
Publisher
Research Nexus Africa’s Networks in Conjunction with The African Institute of Development Informatics & Policy (AIDIP) Ghana & The International Centre for Information Technology & Development (ICITD), USA
Abstract
The aim of developing an IDS is to build a system that oversee the general protection of a network from attacks both from withinand without, and doing so accurately. Optimization of IDS has also being receiving attention from the research community due toits large volumes of security audit data. In developing an IDS, most dataset used have high dimension in which only few attributes are needed for building an IDS – feature selection is used to solve this little problem. In this paper, we present and analyze the performance of some machine learning algorithm which performs classification via clustering using the KDDcup’99 dataset. Using the WEKA tool, simulations were ran and results was deduced after applying the proposed models to the dataset containing all the type of attacks
Description
Keywords
Machine Learning, Data Mining, Network security
Citation
1. Balogun, A. O., Balogun, A. M., Adeyemo, V. E., & Sadiku, P. O. (2015): A Network Intrusion Detection System: Enhanced Classification via Clustering Model. Computing, Information System Development Informatics & Allied Research Journals. 6(4):53-58