SOCIAL, EMOTIONAL, COGNITIVE, AND PHYSIOLOGICAL CORRELATES OF ELECTRONIC SOCIAL BEHAVIOR

There has been very little research that has studied the capacities that can be fostered to mitigate the risk for involvement in electronic bullying or victimization and almost no research examining positive electronic behavior. The primary goal of this dissertation was to use the General Aggression Model and Anxious Apprehension Model of Trauma to explore the underlying cognitive, emotional, and self-regulation processes that are related to electronic bullying, victimization, and prosocial behavior. In Study 1, we explored several potential interpretations of the General Aggression Model that would accurately describe the relationship that electronic self-conscious appraisal, cognitive reappraisal, and activational control may have with electronic bullying and victimization. In Study 2, we used the Anxious Apprehension Model of Trauma to explore rejection cognitions as the mediator of the relationships among emotionality (emotionality, shame, state emotion responses, and physiological arousal) and electronic bullying and victimization using structural equation modelling. In addition, we explored the role of rejection cognitions in mediating the relationship of moral disengagement with electronic bullying. In Study 3, we examined predictors of electronic prosocial behavior, such as bullying, victimization, time online, electronic proficiency, electronic self-conscious appraisals, emotionality, and self-regulation. All three studies supported the General Aggression Model as a framework to guide the study of electronic behavior, and suggest the importance of cognitive, emotional, and behavioral means of regulation in shaping electronic behavior. In addition, each study has implications for the development of high quality electronic bullying prevention and intervention research.

A Cloud-based Framework for Security Analysis of Browser Extensions

In today's internet world, web browsers are an integral part of our day-to-day activities. Therefore, web browser security is a serious concern for all of us. Browsers can be breached in different ways. Because of the over privileged access, extensions are responsible for many security issues. Browser vendors try to keep safe extensions in their official extension galleries. However, their security control measures are not always effective and adequate. The distribution of unsafe extensions through different social engineering techniques is also a very common practice. Therefore, before installation, users should thoroughly analyze the security of browser extensions. Extensions are not only available for desktop browsers, but many mobile browsers, for example, Firefox for Android and UC browser for Android, are also furnished with extension features. Mobile devices have various resource constraints in terms of computational capabilities, power, network bandwidth, etc. Hence, conventional extension security analysis techniques cannot be efficiently used by end users to examine mobile browser extension security issues. To overcome the inadequacies of the existing approaches, we propose CLOUBEX, a CLOUd-based security analysis framework for both desktop and mobile Browser EXtensions. This framework uses a client-server architecture model. In this framework, compute-intensive security analysis tasks are generally executed in a high-speed computing server hosted in a cloud environment. CLOUBEX is also enriched with a number of essential features, such as client-side analysis, requirements-driven analysis, high performance, and dynamic decision making. At present, the Firefox extension ecosystem is most susceptible to different security attacks. Hence, the framework is implemented for the security analysis of the Firefox desktop and Firefox for Android mobile browser extensions. A static taint analysis is used to identify malicious information flows in the Firefox extensions. In CLOUBEX, there are three analysis modes. A dynamic decision making algorithm assists us to select the best option based on some important parameters, such as the processing speed of a client device and network connection speed. Using the best analysis mode, performance and power consumption are improved significantly. In the future, this framework can be leveraged for the security analysis of other desktop and mobile browser extensions, too.