Shane Johnson

Antonis Papasavva, Shane Johnson, Ed Lowther et al.

Fraud is a prevalent offence that extends beyond financial loss, causing psychological and physical harm to victims. The advancements in online communication technologies alowed for online fraud to thrive in this vast network, with fraudsters increasingly using these channels for deception. With the progression of technologies like AI, there is a growing concern that fraud will scale up, using sophisticated methods, like deep-fakes in phishing campaigns, all generated by language generation models like ChatGPT. However, the application of AI in detecting and analyzing online fraud remains understudied. We conduct a Systematic Literature Review on AI and NLP techniques for online fraud detection. The review adhered the PRISMA-ScR protocol, with eligibility criteria including relevance to online fraud, use of text data, and AI methodologies. We screened 2,457 academic records, 350 met our eligibility criteria, and included 223. We report the state-of-the-art NLP techniques for analysing various online fraud categories; the training data sources; the NLP algorithms and models built; and the performance metrics employed for model evaluation. We find that current research on online fraud is divided into various scam activitiesand identify 16 different frauds that researchers focus on. This SLR enhances the academic understanding of AI-based detection methods for online fraud and offers insights for policymakers, law enforcement, and businesses on safeguarding against such activities. We conclude that focusing on specific scams lacks generalization, as multiple models are required for different fraud types. The evolving nature of scams limits the effectiveness of models trained on outdated data. We also identify issues in data limitations, training bias reporting, and selective presentation of metrics in model performance reporting, which can lead to potential biases in model evaluation.

Felix Soldner, Bennett Kleinberg, Shane Johnson

The popularity of online shopping is steadily increasing. At the same time, fake product reviewsare published widely and have the potential to affect consumer purchasing behavior. In response,previous work has developed automated methods for the detection of deceptive product reviews.However, studies vary considerably in terms of classification performance, and many use data thatcontain potential confounds, which makes it difficult to determine their validity. Two possibleconfounds are data-origin (i.e., the dataset is composed of more than one source) and productownership (i.e., reviews written by individuals who own or do not own the reviewed product). Inthe present study, we investigate the effect of both confounds for fake review detection. Using anexperimental design, we manipulate data-origin, product ownership, review polarity, and veracity.Supervised learning analysis suggests that review veracity (60.26 - 69.87%) is somewhat detectablebut reviews additionally confounded with product-ownership (66.19 - 74.17%), or with data-origin(84.44 - 86.94%) are easier to classify. Review veracity is most easily classified if confounded withproduct-ownership and data-origin combined (87.78 - 88.12%), suggesting overestimations of thetrue performance in other work. These findings are moderated by review polarity.

Matt Wixey, Shane Johnson, Emiliano De Cristofaro

Sound at frequencies above (ultrasonic) or below (infrasonic) the range of human hearing can, in some settings, cause adverse physiological and psychological effects to individuals. In this paper, we investigate the feasibility of cyber-attacks that could make smart consumer devices produce possibly imperceptible sound at both high (17-21kHz) and low (60-100Hz) frequencies, at the maximum available volume setting, potentially turning them into acoustic cyber-weapons. To do so, we deploy attacks targeting different smart devices and take sound measurements in an anechoic chamber. For comparison, we also test possible attacks on traditional devices. Overall, we find that many of the devices tested are capable of reproducing frequencies within both high and low ranges, at levels exceeding those recommended in published guidelines. Generally speaking, such attacks are often trivial to develop and in many cases could be added to existing malware payloads, as they may be attractive to adversaries with specific motivations or targets. Finally, we suggest a number of countermeasures, both platform-specific and generic ones.