Paper Title
Identifying The Problem & Solution Of False Positive

We know that, Intrusion Detection System (IDS) has become an integral part of any network. They became easy way to detect anomalies. What is required now is to have an efficient system having high accuracy and detection rate as well as low false alarm rate. Most of the previously proposed methods suffer from the drawback of k-means method with low detection rate and high false alarm rate. In this paper, we are considering one scenario of false positive. The false positive is the case in which the normal data is detected as attack. We are focusing on this problem with one example & proposing one solution for the same problem. The KDD CUP 1999 data set is used. Experimental results show that the class consider as an anomaly class if it have high number of count. But if the true person is crossing the threshold value of count it will be count as anomaly. So, to detect the true person & to remove false positive, we have proposed one solution.