Automation of cyber penetration testing using the detect, identify, predict, react intelligence automation model
Kendra Deptula · Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School) · 2013
The design and implementation of a systems approach to a scalable, standardized automated cyber penetration testing system using the Detect, Identify, Predict, React (DIPR) intelligence automation model and data interoperability standards is the focus of this thesis. The system fuses information from multiple freeware programs that can be thought of as cyber sensors into an interoperable, robust whole in a manner that can tailor itself and learn over time. The groundwork is laid for an enduring system that can adapt to changing systems and vulnerabilities. A barebones proof-of-concept system is implemented and tested using NMap and Ettercap with the proposed DIPR XML file formats as the data intelligence automation standardization mechanism. By implementing this automated cyber penetration system, labor-intensive and costly cyber penetration testing can be simplified by reducing the amount of hand coding and manual testing.