Detection and prediction of anomalous behaviors of enterprise's employees based on data-mining and optimization algorithm.

ABSTRACT: Internal employees have always been at the core of organizational security management challenges. Once an employee exhibits behaviors that threaten the organization, the resulting damage can be profound. Therefore, analyzing reasonably stored behavioral data can equip managers with effective threat monitoring and warning solutions. Through data-mining, a knowledge graph for internal threat data is deduced, and models for detecting anomalous behaviors and predicting resignations are developed. Initially, data-mining is employed to model the knowledge ontology of internal threats and construct the knowledge graph; subsequently, using the behavioral characteristics, the BP neural network is optimized with the Sparrow Search Algorithm (SSA), establishing a detection model for anomalous behavio

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11291484 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature