An Improved Techniques Based on Naive Bayesian for Attack Detection

With the enormous growth of computer networks and the huge increase in the number of applications that rely on it, network security is gaining increasing importance. Moreover, almost all computer systems suffer from security vulnerabilities which are both technically difficult and economically costly to be solved by the manufacturers. Therefore, the role of Intrusion Detection Systems (IDSs), as special-purpose devices to detect anomalies and attacks in a network, is becoming more important. The naive Bayesian Classification is use for intrusion detection system. One of the most important deficiencies in the KDD99 data set is the huge number of redundant records, which causes the learning algorithms to be biased towards the frequent records, and thus prevent them from learning infrequent records, which are usually more harmful to networks such as U2R and R2L attacks.NSL KDD data set have less redundant record .In this paper one algorithm is use for ids naïve Bayesian classification.NSL KDD data set is firstly categorized into numeric form and fuzzy logic is use to fuzzyfied data and apply in all field and then feature is selected using information gain concept .according to best feature gain the reduce field is selected .the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources. Proposed Naïve Bayesian classifier gives higher detection rate and reduce false alarm. Keywords-detection rate, false positive, fuzzy logic, naïve Bayesian, mat lab, IDS.