People tend to disclose personal identifiable information (PII) that could be used by cybercriminals against them. Often, persuasion techniques are used by cybercriminals to trick people to disclose PII. This research investigates whether people can be made less susceptible to persuasion by reciprocation (i.e., making people feel obligated to return a favour) and authority, particularly in regard to whether information security knowledge and positive affect moderate the relation between susceptibility to persuasion and disclosing PII. Data are used from a population-based survey experiment that measured the actual disclosure of PII in an experimental setting (N = 2426). The results demonstrate a persuasion–disclosure link, indicating that people disclose more PII when persuaded by reciprocation, but not by authority. Knowledge of information security was also found to relate to disclosure. People disclosed less PII when they possessed more knowledge of information security. Positive affect was not related to the disclosure of PII. And contrary to expectations, no moderating effects were found of information security knowledge nor positive affect on the persuasion–disclosure link. Possible explanations are discussed, as well as limitations and future research directions. Uitgegeven door Sage, APA beschrijving: van der Kleij, R., van ‘t Hoff—De Goede, S., van de Weijer, S., & Leukfeldt, R. (2023). Social engineering and the disclosure of personal identifiable information: Examining the relationship and moderating factors using a population-based survey experiment. Journal of Criminology, 56(2-3), 278-293. https://doi.org/10.1177/26338076231162660
People tend to disclose personal identifiable information (PII) that could be used by cybercriminals against them. Often, persuasion techniques are used by cybercriminals to trick people to disclose PII. This research investigates whether people can be made less susceptible to persuasion by reciprocation (i.e., making people feel obligated to return a favour) and authority, particularly in regard to whether information security knowledge and positive affect moderate the relation between susceptibility to persuasion and disclosing PII. Data are used from a population-based survey experiment that measured the actual disclosure of PII in an experimental setting (N = 2426). The results demonstrate a persuasion–disclosure link, indicating that people disclose more PII when persuaded by reciprocation, but not by authority. Knowledge of information security was also found to relate to disclosure. People disclosed less PII when they possessed more knowledge of information security. Positive affect was not related to the disclosure of PII. And contrary to expectations, no moderating effects were found of information security knowledge nor positive affect on the persuasion–disclosure link. Possible explanations are discussed, as well as limitations and future research directions. Uitgegeven door Sage, APA beschrijving: van der Kleij, R., van ‘t Hoff—De Goede, S., van de Weijer, S., & Leukfeldt, R. (2023). Social engineering and the disclosure of personal identifiable information: Examining the relationship and moderating factors using a population-based survey experiment. Journal of Criminology, 56(2-3), 278-293. https://doi.org/10.1177/26338076231162660
Game development businesses often choose Lua for separating scripted game logic from reusable engine code. Lua can easily be embedded, has simple interfaces, and offers a powerful and extensible scripting language. Using Lua, developers can create prototypes and scripts at early development stages. However, when larger quantities of engine code and script are available, developers encounter maintainability and quality problems. First, the available automated solutions for interoperability do not take domain-specific optimizations into account. Maintaining a coupling by hand between the Lua interpreter and the engine code, usually in C++, is labour intensive and error-prone. Second, assessing the quality of Lua scripts is hard due to a lack of tools that support static analysis. Lua scripts for dynamic analysis only report warnings and errors at run-time and are limited to code coverage. A common solution to the first problem is developing an Interface Definition Language (IDL) from which ”glue code”, interoperability code between interfaces, is generated automatically. We address quality problems by proposing a method to complement techniques for Lua analysis. We introduce Lua AiR (Lua Analysis in Rascal), a framework for static analysis of Lua script in its embedded context, using IDL models and Rascal.
