Date Approved
2026
Degree Type
Open Access Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department or School
College of Engineering and Technology
Committee Member
Bilquis Ferdousi, PhD
Committee Member
Dorothy McAllen, PhD
Committee Member
Robert Carpentar, PhD
Committee Member
Omar Darwish, PhD
Abstract
As cyberattack tools and techniques get sophisticated and persistent, reactive cybersecurity has been unable to effectively prevent breaches and compromises. Organizations and institutions with large complex environments have wide vulnerable exposure with higher chances of a cyberattack. This also increases overall security and financial risk. Possibility of repetitive attacks from cyber threats increases, too, despite the use of standard defense measures. Security tools and system vulnerabilities often need manual patching and updates to keep the environment secure and functioning effectively. Productivity suffers from manual trade-offs in keeping the systems secure from adverse impact. Persistent high-severity cyberattacks, despite continued reactive defensive efforts, are devastating to an organization across any industry. This study explores the possibility of building a proactive self-arming autonomous cyber defense ecosystem. It analyzes various dependent and independent characteristics of detection and prevention defensive measures. These characteristics include security vulnerability exploitability, cyber threat intelligence with atomic and behavioral indicators from past cyberattacks, with possibility of recurring impact across any industry. The data collected from secondary datasets with these characteristics helped analyze the cybersecurity ecosystem. Statistical correlations and predictive analysis with industry agnostic context, automated vulnerability management, with exploitability is used to proactively reduce impact from cyberattacks. It is observed that cyber threat intelligence with defense in depth and breadth helps dynamically tune defenses to increase attacker pain. The analytics from an assumed breach hypothesis model can prove that AI based generative models can effectively predict and provide autonomous decisions to proactively stop cyberattacks.
Recommended Citation
Chavarkar, Yogesh, "Towards breach hypothesis based predictive autonomous cyber defense ecosystem: Proactive prevention of imminent threats and productivity losses" (2026). Master's Theses and Doctoral Dissertations. 1341.
https://commons.emich.edu/theses/1341