Network Intrusion Awareness using Data Fusion and SVM Classification

Network intrusion awareness is important factor for risk analysis of network security. In the recent decade different method and framework are available for intrusion detection and security alertness. A number of method based on knowledge discovery process and some framework based on neural network. These complete model take rule based decision for the generation of security alerts. In this dissertation we proposed a novel method for intrusion awareness using data fusion and SVM classification. The data fusion work on the biases of features gathering of occurrence. Support vector machine is super classifier of data. Now we used SVM for the detection of closed item of ruled based technique.