A Novel Framework for Detection and Prevention of JavaScript-XSS Attacks in Android based Hybrid Applications

The development and analysis of mobile applications have become an active research area. The concept of hybrid applications has emerged in the last three years where applications are developed in both native and web languages. The use of web languages raises certain security risks in hybrid mobile applications as it creates possible channels where malicious code can be injected inside the application. In hybrid mobile applications, WebView, an important component, implements the sandbox mechanism to protect the local resources of smartphone devices from unauthorized access of JavaScript. The WebView application program interfaces has security issues as the attackers can attack the hybrid application via JavaScript code by bypassing the sandbox security by accessing the public methods of the applications. Cross-Site Scripting is one of the most popular malicious code injection techniques for accessing the public methods of the application through JavaScript. This research study proposes the framework for the detection and prevention of XSS attacks in hybrid applications using state of the art Machine Learning algorithms. The proposed framework detects the attacks using the registered Java object features. The dataset and the sample hybrid applications have been developed using the android studio. Then the widely used RapidMiner toolkit has been used for empirical analysis. The results reveal that ensemble based Random Forest algorithm outperforms other algorithms and achieves the accuracy and F-measures as high as of 99%. The proposed framework can be helpful to detect and prevent XSS based attacks on hybrid applications in the future.