Intrusion detection using data mining with correlation

The biggest concern of Network is security. Intro find the tricks and tools of the Attackers. Data Mining techniques automatically learn the pattern of the tuples and Intelligent decision are made. Supervised learning methods finds the attack based on previous knowledge and unknown attacks are detected by using Unsupervised learning. Dos, Probe and Normal data are correctly detected by maximum Data Mining algorithms, whereas True Positive Rate of R2L and U2R are very low. The paper highlight the best attribute selection method which helps to improve the accuracy of the algorithms. The Hybrid methods (K-means + ID3 and K-means + Support vector machine) are used to improve True Positive Rate of R2L attacks. NSL_KDD Training and Testing dataset are used.