Towards breach hypothesis based predictive autonomous cyber defense ecosystem: Proactive prevention of imminent threats and productivity losses

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Open Access Dissertation

Discipline thesis:degree_discipline

College of Engineering and Technology

Year dc:date.available

2026

Author dc:creator

• Chavarkar, Yogesh

Contributors dc:contributor

• Bilquis Ferdousi, PhD
• Dorothy McAllen, PhD
• Robert Carpentar, PhD

• Cyber Threat Intelligence
• Cybersecurity Incident Response
• Cybersecurity Threat Hunting
• Proactive Cyberattack Prevention
• Security Orchestration Automated Response
• Vulnerability Enumeration
• Computer Sciences
• Cybersecurity

Repository record dc:identifier

https://commons.emich.edu/theses/1341

OAI identifier oai:identifier

oai:commons.emich.edu:theses-2674