Novel Approaches for Access Level Modelling of Employees in an Organization Through Machine Learning

In the contemporary business landscape, organizational trustworthiness is of utmost importance. Employee behavior, a pivotal aspect of trustworthiness, undergoes analysis and prediction through data science methodologies. Simultaneously, effective control over employee access within an organization is imperative for security and privacy assurance. This research proposes an innovative approach to model employee access levels using Geo-Social data and machine learning techniques like Linear Regression, K-Nearest Neighbours, Decision Tree, Random Forest, XGBoost, and Multi-Layered Perceptron. The data, sourced from social and geographical realms, encompasses details on employee geography, navigation preferences, spatial exploration, and choice set formations. Utilizing this information, a behavioral model is constructed to assess employee trustworthiness, categorizing them into access levels: low, moderate, high, and very high. The model's periodic review ensures adaptive access level adjustments based on evolving behavioral patterns. The proposed approach not only cultivates a more trustworthy organizational network but also furnishes a precise and reliable trustworthiness evaluation. This refinement contributes to heightened organizational coherence, increased employee commitment, and reduced turnover. Additionally, the approach ensures enhanced control over employee access, mitigating the risks of data breaches and information leaks by restricting the access of employees with lower trustworthiness.

@article{Hiremath2024,
  title     = {Novel Approaches for Access Level Modelling of Employees in an Organization Through Machine Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Priyanka C Hiremath and Raju G T},
  doi       = {10.14569/IJACSA.2024.0150409},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150409}
}