<![CDATA[Internet increasing is also exponentially increasing intrusion or attacks by crackers exploit vulnerabilities in Internet protocols, operating systems and software applications. Intrusion or attacks against computer networks, especially the Internet has increased from year to year. Intrusion detection systems into the main stream in the information security. The main purpose of intrusion detection system is a computer system to help deal with the attack. This study presents a correlation-based feature selection to detect computer network intrusions. Feature selection result applied on naïve bayes algorithm. Performance is measured based on the level of accuracy, sensitivity, precision and spesificity. Dataset used in this study is a dataset KDD 99 intrusion detection system. Dataset is composed of two training data and testing data. From the experimental results obtained by the accuracy of naïve Bayes without feature selection 76,12 %, and the accuracy with feature selection 81,89 %. Correlaiton-based feature selection can improve naïve bayes accuration. Keyword: naïve bayes, intrusion detection, correlation-based fetaure selection]]>
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