Authors : K.G. Maheswari and R. Anita
Abstract: The rapid growth of the web applications are resulted in severe security issues which gives out various classifications of attacks related with web usages. These attacks are generalized by different characteristics and methods to make the system vulnerable for the easy injection of threats. In this study, the mixed case analysis using distance metrics is designed to classify the various types of web attacks based on the severity of the vulnerability. The set of network and web related attributes are taken from the renowned datasets which is dynamically stored in the log server for the future reference. Hence, these datasets are extracted for the detection system by classifying the attack, instantaneously generates the classes of data clusters. These clusters are used for learning metric in mixed cases for analysing the web related attacks in the renowned datasets.
K.G. Maheswari and R. Anita, 2015. Generalization of Minkowski Distance Metrics in Mixed Case Analysis for Web Intrusion Detection System. International Journal of Soft Computing, 10: 274-278.