Three essays on socially engineered attacks : the case of online romantic scams
Bibliographic record
Abstract
The Internet has transformed the way people initiate and nurture romantic relationships. With the continued adoption of social media and online dating platforms, love, literally, is in the air. A recent report indicates that 30% of the U.S. adults have experience using online dating platforms, and 11% of the U.S. adults have used the platform in the past year. However, cybercriminals see a massive opportunity to defraud this emerging demography of online daters by launching online romance scams. The scammers pretend to engage in a romantic relationship with the victim through online platforms and eventually defraud the victim financially. Online romance scam became apparent around 2008, and now it is one of the widely reported cybercrimes. People from developed countries such as the U.S., the U.K., Canada, and Australia face millions of dollars in financial loss from online romance scams, as evident from the public agency reports. In addition to the financial loss, online romance scam victims face significant emotional loss and psychological distress from the betrayal by someone they love. Extant research in information systems, cybersecurity, and criminology investigates online romance scams extensively to better understand this phenomenon. Current literature on online romance scam studies the process of scam, predictors of scam victimization, persuasion and deception techniques used by scammers, human and technical level prevention mechanisms, and rationalizing of the scam from the offender perspective. The first two essays of this dissertation look into two significant but understudied aspects of online romance scam: the impact of psychological stressors around online romance scam on online dating psychological capital and the impact of scammer’s representation of love on online romance scam gullibility of the victims. In the first essay, we integrate the Etiology of Fear Theory, Broaden-Build Theory, and Fear of Crime Framework to check how online dater’s negative psychological states such as anxiety, cognitive vulnerability, social vulnerability, and victimization fear directly or indirectly reduce positive psychology in online dating. This essay employs a sequential mixed-method design with a qualitative phase followed by a quantitative survey. Drawing from the Triangle Theory of Love, Social Exchange Theory, and Theory of Mood-Congruent Judgement, the second essay argues that if the scammers show a higher degree of love in the scam grooming stage, then the victim will be more gullible to fall for the online romance scam through the mechanisms of relationship trust and relationship satisfaction. This essay runs two scenario-based experiments to test the hypotheses. The third essay focuses on the impact of state-level cybercrime governance measure namely cybersecurity taskforce in reducing metro city-level social engineering frauds, including online romance scams. This essay uses a 10-year panel data from the U.S. to conduct a nationwide quasi-experiment. The results show state-level cybersecurity taskforce has deterrence effect in reducing social engineering fraud in metro city-level only in the states where the governance complexity is low. The empirical findings are consistent with the concepts of Stackelberg Security Game. Each essays outlines theoretical and managerial implications. The overarching theoretical contributions of this dissertation are finding – 1) how the negative emotional experiences surrounding online romance scam have detrimental effect on the positive experience on online dating, 2) how scammer’s grooming technique leads to online romance scam gullibility of the victims, and 3) how state-level governance can deter social engineering frauds including online romance scam. The findings of the essays will be useful for the online daters, online dating platforms, and regulatory authorities to make the online dating space a safer place to initiate and nurture romantic relationships and to reduce the economic losses from social engineering attacks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".