Human or AI? Using Digital Behavior to Verify Essay Authorship

Large language models (LLMs) such as OpenAI\u27s GPT-4 have transformed natural language processing with their ability to understand context and generate human-like text. This has led to considerable debate, especially in the education sector, where LLMs can enhance learning but also pose challenges to academic integrity. Detecting AI-generated content (AIGC) is difficult, as existing methods struggle to keep pace with advancements in generation technology. This research proposes a novel approach to AIGC detection in short essays, using digital behavior capture and follow-up questioning to verify text authorship. We executed a controlled experiment as an initial evaluation to test the prototype system. The results obtained show promise in differentiating between user-authored and AI-generated text. The system design and prototype represent valuable contributions for future research in this area. The solution also provides a novel approach to addressing practical challenges posed by LLMs, particularly in maintaining academic integrity in educational settings

Detection of advanced web bots by combining web logs with mouse behavioural biometrics

Web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint, support the main browser functionalities, and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour that reduce their detectability. This work proposes a web bot detection framework that comprises two detection modules: (i) a detection module that utilises web logs, and (ii) a detection module that leverages mouse movements. The framework combines the results of each module in a novel way to capture the different temporal characteristics of the web logs and the mouse movements, as well as the spatial characteristics of the mouse movements. We assess its effectiveness on web bots of two levels of evasiveness: (a) moderate web bots that have a browser fingerprint and (b) advanced web bots that have a browser fingerprint and also exhibit a humanlike behaviour. We show that combining web logs with visitors’ mouse movements is more effective and robust toward detecting advanced web bots that try to evade detection, as opposed to using only one of those approaches