Machine-Learning-based User Behavior Classification for Improving Security Awareness Provision

Users of information technology are regarded as essential components of information security. Users’ lack of cybersecurity awareness can result in external and internal security attacks and threats in any organization that has several users or employees. Although various security methods have been designed to protect organizations from external intrusions and attacks, the human factor is also essential because security risks by “insiders” can occur due to a lack of awareness. Therefore, instead of general nontargeted security training, comprehensive cybersecurity awareness should be provided based on employees’ online behavior. This study seeks to provide a machine-learning-based model that provides user behavior analysis in which organizations can profile their employees by analyzing their online behavior to classify them into different classes and, thus, help provide them with appropriate awareness sessions and training. The model proposed in this paper will be evaluated and assessed through its implementation on a sample dataset that reflects users’ online activities over a specific period to measure the model’s accuracy and effectiveness. A comparison between six classification techniques has been made, and random forest classification had the best performance regarding classification accuracy and performance time. After users are classified, each group can be provided with the appropriate training material. This study will stimulate additional research in this area, which has not been widely investigated, and it will provide a useful point of reference for other studies. Additionally, it should provide insightful information to help decision-makers in organizations provide necessary and effective security awareness.

@article{Al-Mashhour2023,
  title     = {Machine-Learning-based User Behavior Classification for Improving Security Awareness Provision},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Alaa Al-Mashhour and Areej Alhogail},
  doi       = {10.14569/IJACSA.2023.0140819},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140819}
}