An integration of k-means clustering and naïve bayes classifier for Intrusion Detection

摘要: Static security mechanisms such as firewalls can provide a reasonable level of security, but dynamic like Intrusion Detection Systems (IDSs) should also be used. Different intrusion detection techniques employed to search for attack patterns in the observed data. Misuse and anomaly are most commonly used techniques. But they have their own disadvantages. To overcome those issues, hybrid methods Hybrid classifiers able improved accuracy, complex structure high computational cost. Hence new learning method, that integrates k-means clustering naive bayes classification, has been introduced. A relation between distances from each data sample number centroids found by algorithm is This form features, based on features original set. These distance sum-based then classifier training detection.