Social networks

This project examines three properties of underground cybercrime communities: 1) profitability, 2) connectivity, 3) and sustainability. It identifies qualitative and quantitative metrics for these properties as well as discusses the relative effectiveness of distinct operationalization of these metrics under different levels of data granularity. The goal is to develop metrics that provide meaning indicators even when data is limited. for example, if public posts are available but not private messages between individual cybercriminals.

The modernized Smart Grid (SG) is expected to enable several new applications such as dynamic pricing, demand response and fraud detection; however, collection of such fine-grained data raises privacy issues. This project aims to design and implement several novel mechanisms for securing data collection and communication in SG Advanced Metering Infrastructure applications while preserving user privacy when the data are to be accessed.

This research project studies a new area of research - exposure detection - that is at the intersection of data mining, security, and natural language processing. Exposure detection refers to discovering components/attributes of a user's public profile that reduce the user's privacy. To help the public understand the privacy risks of sharing certain information on the web, this research project focuses on developing efficient algorithms for modeling how an adversary learns information using incomplete and schemaless public data sources.

The "Virtual Laboratory and Curriculum Development for Secure Mobile Computing" project at the University of Texas at Dallas (UTD) will develop a set of courses and a virtual laboratory in mobile system security with an emphasis on securing smart phones. The courses that will be developed will include topics such as Android taint analysis using existing tools or development of new tools, scalable Android security threat analysis on applications (apps), and smart phone forensics.

Online social networks (OSNs) face various forms of fraud and attacks, such as spam, denial of service, Sybil attacks, and viral marketing. In order to build trustworthy and secure OSNs, it has become critical to develop techniques to analyze and detect OSN fraud and attacks. Existing OSN security approaches usually target a specific type of OSN fraud or attack and often fall short of detecting more complex attacks such as collusive attacks that involve many fraudulent OSN accounts, or dynamic attacks that encompass multiple attack phases over time.