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This paper describes a hybrid design for intrusion detection that combines anomaly detection with misuse detection. The proposed method includes an ensemble feature selecting classifier and a data mining classifier. The former consists of four classifiers using different sets of features and each of them employs a machine learning algorithm named fuzzy belief k-NN classification algorithm. The latter...
Various intrusion detection systems (IDSs) reported in the literature have shown distinct preferences for detecting a certain class of attack with improved accuracy, while performing moderately on the other classes. In view of the enormous computing power available in the present-day processors, deploying multiple IDSs in the same network to obtain best-of-breed solutions has been attempted earlier...
Classification of intrusion attacks and normal network traffic is a challenging and critical problem in network security. Many classification methods for intrusion detection have been proposed, but there are few algorithms that are capable of distinguishing among the various attacks and normal connections effectively. This paper presents an effective intrusion detection algorithm based on conscientious...
Anomaly detection in computer networks tries to detect traffic deviation from the normal model. Traditionally, feature-based one-class classifiers are the main components of anomaly detection systems. The performance of this anomaly detection system largely depends on the result of the feature selection. dissimilarity representations describe an object by its dissimilarities to a set of target class...
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