Kashif Inayat

Usama Khalid, Muhammad Abdullah, Kashif Inayat

The development and analysis of mobile applications in term of security have become an active research area from many years as many apps are vulnerable to different attacks. Especially the concept of hybrid applications has emerged in the last three years where applications are developed in both native and web languages because 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. WebView is an important component in hybrid mobile applications which used to implements a sandbox mechanism to protect the local resources of smartphone devices from un-authorized access of JavaScript. However, the WebView application program interfaces (APIs) also have security issues. For example, an attacker can attack the hybrid application via JavaScript code by bypassing the sandbox security through accessing the public methods of the applications. Cross-site scripting (XSS) is one of the most popular malicious code injection technique for accessing the public methods of the application through JavaScript. This research proposes a framework for detection and prevention of XSS attacks in hybrid applications using state-of-the-art machine learning (ML) algorithms. The detection of the attacks have been perform by exploiting the registered Java object features. The dataset and the sample hybrid applications have been developed using the android studio. Then the widely used toolkit, RapidMiner, has been used for empirical analysis. The results reveal that the ensemble based Random Forest algorithm outperforms other algorithms and achieves both the accuracy and F-measures as high as of 99%.

Shahbano, Muhammad Abdullah, Kashif Inayat

In recent decades, the automatic video surveillance system has gained significant importance in computer vision community. The crucial objective of surveillance is monitoring and security in public places. In the traditional Local Binary Pattern, the feature description is somehow inaccurate, and the feature size is large enough. Therefore, to overcome these shortcomings, our research proposed a detection algorithm for a human with or without carrying baggage. The Local tri-directional pattern descriptor is exhibited to extract features of different human body parts including head, trunk, and limbs. Then with the help of support vector machine, extracted features are trained and evaluated. Experimental results on INRIA and MSMT17 V1 datasets show that LtriDP outperforms several state-of-the-art feature descriptors and validate its effectiveness.