• Yogadhar Pandey
• Shailendra Singh

With rapid development and extensive growth of information systems, security problems increasingly bring into prominence.IDS are trained for classes: normal and attacks and further for types of attacks. A data set is called imbalanced when its elements are not evenly divided among the classes and this problem is a difficult challenge faced by machine learning and data mining researchers. Sampling techniques are one of the solutions to this problem. This paper is an attempt to provide an efficient method for imbalanced dataset classification with effect of sampling and feature selection. Proposed class balancing approach improve the performance of the classifier, shows better accuracy and reduced false alarm rate when compared with existing approach. Our proposed approach has been tested using KDD CUP'99 dataset.

IDS;Sampling;Machine Learning ;Classification;Class Balancin;KDD’99Dataset

"Enhanced Network Intrusion Detection System for Imbalance Dataset based on Feature Selection and Classification Algorithms ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.164-172, November-2018, Available :http://www.jetir.org/papers/JETIR1907621.pdf

"Enhanced Network Intrusion Detection System for Imbalance Dataset based on Feature Selection and Classification Algorithms ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp164-172, November-2018, Available at : http://www.jetir.org/papers/JETIR1907621.pdf