Data leakage detection using dynamic data structure and classification techniques

Data leakage is a permanent problem in public and private institutions around the world; particularly, identifying the information leakage efficiently. In order to solve this problem, this paper poses an adaptable data structure based on human behavior using all the activities executed within the computer system. When applying this structure, the normal behavior is modeled for each user, so in this way, detects any abnormal behavior in real time. Moreover, this structure enables the application of several classification techniques such as decision trees (C4.5), UCS, and Naive Bayes, these techniques have proven efficient outcomes in intrusion detection. In the testing of this model, a scenario demonstrating the proposal’s effectiveness with real information from a government institution was designed so as to establish future lines of work.

• Autores: Guevara Maldonado, César Byron
• Tipo de recurso: Article of journal
• Fecha de publicación: 2015
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/1748
• Acceso en línea: https://hdl.handle.net/11323/1748; https://repositorio.cuc.edu.co/
• Palabra clave: Data Leakage; Data Structure; Decision Tree C4.5; UCS; Naive Bayes; Fuga de Información; Estructura de Datos; Árbol de decisión
• Rights: openAccess
• License: http://purl.org/coar/access_right/c_abf2