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Kim, Dae WookData-Driven Network-Centric Threat Assessment
Doctor of Philosophy (PhD), Wright State University, 2017, Computer Science and Engineering PhD
As the Internet has grown increasingly popular as a communication and information sharing platform, it has given rise to two major types of Internet security threats related to two primary entities: end-users and network services. First, information leakages from networks can reveal sensitive information about end-users. Second, end-users systems can be compromised through attacks on network services, such as scanning-and-exploit attacks, spamming, drive-by downloads, and fake anti-virus software. Designing threat assessments to detect these threats is, therefore, of great importance, and a number of the detection systems have been proposed. However, these existing threat assessment systems face significant challenges in terms of i) behavioral diversity, ii) data heterogeneity, and iii) large data volume. To address the challenges of the two major threat types, this dissertation offers three unique contributions. First, we built a new system to identify network users via Domain Name System (DNS) traffic, which is one of the most important behavior-based tracking methods for addressing privacy threats. The goal of our system is to boost the effectiveness of existing user identification systems by designing effective fingerprint patterns based on semantically limited DNS queries that are missed by existing tracking efforts. Second, we built a novel system to detect fake anti-virus (AV) attacks, which represent an active trend in the distribution of Internet-based malware. Our system aims to boost the effectiveness of existing fake AV attack detection by detecting fake AV attacks in three challenging scenarios: i) fake AV webpages that require user interaction to install malware, instead of using malicious content to run automatic exploitation without users consent (e.g., shellcode); ii) fake AV webpages designed to impersonate real webpages using a few representative elements, such as the names and icons of anti-virus products from authentic anti-virus webpages; and iii) fake AV webpages that offer up-to-date solutions (e.g.,product versions and threat names) to emerging threats. Finally, we built a novel system to detect malicious online social network (OSN) accounts that participate in online promotion events. The goal of our work is to boost the effectiveness of existing detection methods, such as spammer detection and fraud detection. To achieve our goal, our framework that systematically integrates features that characterize malicious OSN accounts based on three of their characteristics: their general behaviors, their recharging patterns, and their currency usage, and then leverages statistical classifier for detection.

Committee:

Junjie Zhang, Ph.D. (Advisor); Adam Robert Bryant, Ph.D. (Committee Member); Bin Wang, Ph.D. (Committee Member); Xuetao Wei, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

network security; fake anti-virus software; intrusion detection; web document analysis; statistical classification; Domain Name System; behavioral fingerprints; privacy; online social networks; virtual currency; malicious accounts

Wolf, Christopher AlexanderCase Histories and Analyses of Synthetic Economies: Implications for Experiments, Game Design, Monetization, and Revenue Maximization.
BBA, Kent State University, 2013, College of Business Administration / Department of Economics
This thesis explores the economic systems of massively multiplayer video games, a young field commonly known as Synthetic Economics. A generalized version of the Faucet-Drain model is introduced to define the nature of synthetic economies, as well as an inflation model under the extreme cases of infinite demand and infinite supply. These models provide the theory necessary to analyze the included cases of virtual currency inflation, virtual item Trivialization, price ceiling-induced shortages, and infinite supply and demand originating from games themselves. In addition, the particularities of browser-interface MMOGs, which were not explored by prior literature, are explicated. The practical focus of this thesis applies the theory and cases to guide sustainable multiplayer game design, choose suitable monetization approaches, and maximize revenue given a particular monetization method. A final case utilizes these applications to sustainably increase the monthly revenue of a browser-interface MMOG. At the time of this thesis' submission, the implemented approach appears to have increased revenue by at least 10 percent. An additional application proposes browser-interface MMOGs as an effective and cost-efficient platform for social science experiments, contrasting the use of traditional MMOGs which have met little success for this purpose.

Committee:

Lucas Engelhardt (Advisor); Leslie Heaphy (Committee Chair); Emmanuel Dechenaux (Committee Member); Victor Berardi (Committee Member)

Subjects:

Economic Theory; Economics

Keywords:

Economics; Synthetic Economics; Synthetic Economy; Monetization; Revenue-Maximization; Analytics; Virtual World; Virtual Currency; Game Design; Price Ceiling; Shortage; Inflation; Hyperinflation; MMOG; Browser-Interface MMOG; PBBG; Black Market