Security Analytics For Heterogeneous Web

Abstract

In recent days, Enterprises are expanding their business efficiently through web applications which has paved the way for building good consumer relationship with its customers. The major threat faced by these enterprises is their inability to provide secure environments as the web applications are prone to severe vulnerabilities. As a result of this, many security standards and tools have been evolving to handle the vulnerabilities. Though there are many vulnerability detection tools available in the present, they do not provide sufficient information on the attack. For the long-term functioning of an organization, data along with efficient analytics on the vulnerabilities is required to enhance its reliability. The proposed model thus aims to make use of Machine Learning with Analytics to solve the problem in hand. Hence, the sequence of the attack is detected through the pattern using PAA and further the detected vulnerabilities are classified using Machine Learning technique such as SVM. Probabilistic results are provided in order to obtain numerical data sets which could be used for obtaining a report on user and application behavior. Dynamic and Reconfigurable PAA with SVM Classifier is a challenging task to analyze the vulnerabilities and impact of these vulnerabilities in heterogeneous web environment. This will enhance the former processing by analysis of the origin and the pattern of the attack in a more effective manner. Hence, the proposed system is designed to perform detection of attacks. The system works on the mitigation and prevention as part of the attack prediction.