LLM-based assessment of HTTPS cybersecurity awareness: Dataset from moroccan web users and webmasters

Area of Science:

• Cybersecurity
• Information Security
• Human-Computer Interaction

Background:

• Effective cybersecurity relies heavily on user awareness, especially concerning the HTTPS protocol.
• Existing research lacks empirical datasets assessing both awareness and implementation of HTTPS security practices.
• This study addresses the gap by providing a dataset on cybersecurity awareness and HTTPS behaviors.

Purpose of the Study:

• To create and analyze a dataset on cybersecurity awareness and HTTPS-related behaviors of Moroccan web users and webmasters.
• To empirically assess awareness levels and implementation practices concerning HTTPS.
• To provide a resource for researchers and practitioners in cybersecurity.

Main Methods:

• A Google Forms survey collected data from 440 voluntary Moroccan participants (web users and webmasters).
• Data included multiple-choice questions and free-text entries, categorized using GPT-4o.
• Analysis involved score-encoding, KMeans clustering for behavioral patterns, and Principal Component Analysis (PCA).

Main Results:

• Participant awareness levels were classified as Low, Moderate, or High.
• KMeans clustering identified distinct behavioral patterns for users and webmasters.
• PCA and LLM interpretation offered insights into awareness profiles and cybersecurity risk behaviors.

Conclusions:

• The dataset provides a comprehensive analysis of cybersecurity awareness and HTTPS behaviors in a specific regional context.
• It serves as a valuable resource for empirical research and practical improvements in web security.
• The findings support the development of role-based and context-specific cybersecurity strategies.