Toward Dynamic Human Knowledge Assessment to Tailor Network Traffic Visual Platform Interfaces

Nowadays, a vast amount of data flows through networks, with many users inadvertently agreeing to their personal data being processed by network providers, frequently lacking comprehension regarding its management or sharing among various entities. Various network data analytics tools have been introduced in recent years, aiming to simplify visual representations for network traffic analysis. However, these platforms often fail to account for the diverse technical knowledge and cyber risk awareness levels of modern Internet users. To address this issue, we propose the introduction of a Large Language Model (LLM)-based conversational agent with a dynamically structured visual platform. This combination allows for the assessment of users’ comprehension through adaptive questioning, enabling personalized interface adaptations that enhance user comprehension and engagement. In this paper, we discuss the architecture and components of the proposed solution, emphasizing the importance of adaptive interfaces in enhancing user experience and fostering security awareness. Through the incorporation of LLMs for human knowledge assessment, our approach endeavors to craft a more personalized and efficient visual platform for analyzing network traffic and cyber threats.