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- Genre: Doctoral Dissertation
Description
Studies on underground forums can significantly advance the understanding of cybercrime workflow and underground economies. However, research on underground forums has concentrated on public information with little attention paid to users’ private interactions. Since detailed information will be discussed privately, the failure to investigate private interactions may miss critical intelligence and even misunderstand the entire underground economy. Furthermore, underground forums have evolved into criminal freelance markets where criminals trade illicit products and cybercrime services, allowing unsophisticated people to launch sophisticated cyber attacks. However, current research rarely examines and explores how criminals interact with each other, which makes researchers miss the opportunities to detect new cybercrime patterns proactively. Moreover, in clearnet, criminals are active in exploiting human vulnerabilities to conduct various attacks, and the phishing attack is one of the most prevalent types of cybercrime. Phishing awareness training has been proven to decrease the rate of clicking phishing emails. However, the rate of reporting phishing attacks is unexpectedly low based on recent studies, leaving phishing websites with hours of additional active time before being detected. In this dissertation, I first present an analysis of private interactions in underground forums and introduce machine learning-based approaches to detect hidden connections between users. Secondly, I analyze how criminals collaborate with each other in an emerging scam service in underground forums that exploits the return policies of merchants to get a refund or a replacement without returning the purchased products. Finally, I conduct a comprehensive evaluation of the phishing reporting ecosystem to identify the critical challenges while reporting phishing attacks to enable people to fight against phishers proactively.
ContributorsSun, Zhibo (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Thesis advisor) / Bao, Tiffany (Committee member) / Benjamin, Victor (Committee member) / Arizona State University (Publisher)
Created2022
Description
Digital media refers to any form of media which depends on electronic devices for its creation, distribution, view, and storage. Digital media analytics involves qualitative and quantitative analysis from the business to understand users’ behaviors. This technique brings disruptive changes to many industries and its path of economic disruption is getting wider and wider. Under the context of the increasingly popular digital media market, this dissertation investigates what are the best content delivery strategy and the new cultural phenomenon: Internet Water Army. The first essay proposes a theory-guided computational approach that consolidates distinct data sources spanning unstructured text, image, and video data, systematically measures modes of persuasion, and unveils the multimedia content design strategies for crowdfunding projects. The second essay studies whether using the Internet Water Army helps sales and under what conditions it helps. This study finds that the Internet water army helps product sales at both post-level and fans-level. The effect is largely reflected by changing the number of emotional fans. Furthermore, the earlier to purchase the water armies, more haters, likers, and neutral fans it can attract. The last essay builds a game model to study the trade- off between honestly promoting the product according to their evaluation and catering to the consumer’s prior belief on the product quality to stay on the market as long as possible. It provides insights on the optimum usage of promotion on social media and demonstrate how conventional wisdom about negative reviews will hurt business may be misleading in the presence of social media. These three studies jointly contribute to the crowdfunding and social media studies literature by elucidating the content delivery strategy, and the impact and purchasing strategy of the Internet Water Army.
ContributorsYIN, XUEYAN (Author) / Chen, Pei-Yu (Thesis advisor) / Gu, Bin (Committee member) / Shi, Zhan (Committee member) / Benjamin, Victor (Committee member) / Arizona State University (Publisher)
Created2020