Effective discriminant function for intrusion detection using SVM

Abstract

Pinpointing the intrusion from the available huge intrusion data is demanding. Intrusion detection is treated as a data analysis problem. The process of finding accurate intrusion is typical in real world scenarios. For improving accurate identification of intrusions data mining approaches are adopted and proved automatic analysis with improved performance. These techniques enhancing the detection rate of the intrusions which is very effective. Discriminant function is very critical in separating the normal and anomaly behavior accurately. The support vector machine based classification algorithm is used to classify the intrusions accurately by using the discriminant function. The effective discriminant function will be accurately identifies the data into intrusion and anomaly. The evaluation of the discriminant is important in the evaluation of the intrusion detection system. Performance of intrusion detection system depends on the choice of the discriminant function.