KDDCUP’99 Network Traffic Data Set | Tuhin Sharma

Dynamic Network Traffic Data Classification for Intrusion Detection Using Genetic Algorithm

Thu, 20 Dec 2012 00:00:00 +0000

Abstract

Intrusion Detection System (IDS) classifies network traffic data either (anomaly( or (normal( to protect computer systems from different types of attacks. In this paper, data mining concepts and genetic algorithm have been applied to classify online traffic data efficiently by developing a rule based lazy classifier. The proposed method updates the rule set dynamically to accommodate the changing pattern in the traffic data in order to attain highest classification accuracy and at the same time maintaining consistency. The classifier is able to detect variants of common network traffic data patterns or modified existing security attacks based on the knowledge gained from its existing training data set with significant classification accuracy.

Generation of Sufficient Cut Points to Discretize Network Traffic Data Sets

Thu, 20 Dec 2012 00:00:00 +0000

Abstract

Classification accuracy and efficiency of an intrusion detection system (IDS) are largely affected by the discretization methods applied on continuous attributes. Cut generation is one of the methods of discretization and by applying variable number of cuts (in a partition) to the continuous attributes, different classification accuracy are obtained. In the paper to maximize accuracy of classifying network traffic data either ‘normal’ or ‘anomaly’, the proposed algorithm determines the set of cut points for each of the continuous attributes. After generation of appropriate and necessary cut points, they are mapped into corresponding intervals following centre-spread encoding technique. The learnt cut points are applied on the test data set for discretization to achieve maximum classification accuracy.