Intrusion Detection using Data Mining

Abstract

Intrusion detection plays very important role in securing information servers. Classification and clustering data mining algorithms are very effective to deal with intrusion detection. However, classification (supervised) results with false negative detection and clustering (unsupervised) results with false positive detection. This paper introduces a unique framework consisting of pre-processing unit, intrusion detection using quad split (IDTQS), intrusion detection using correlation-based quad split (IDTCA) and intrusion detection using clustering (IDTC). In this proposed framework, IDTQS and IDTCA shows accuracy improvement for University of New South Wales (UNSW) dataset is in the range 4%-34% for DoS, probe, R2L, U2R and normal classes. IDTC clustering algorithm performs with 97% accuracy. Training and testing time is improved by 14% for IDTCA in comparison with IDTQS.