From Vulnerability to Vault: Mitigating Risks for Confidential Documents in AR/VR
Creators: name: Kosaraju, Vasavi
Publisher: name: My University
Year: 2024
Subjects: subject: OCR
subject: AR/VR
subject: Cyber security
subject: Insider attack
subject: Threat model
subject: Context aware machine learning
subject: Iris recognition
subject: Electrical Engineering

description: In an era marked by escalating cyber threats targeting financial institutions, government entities, critical infrastructure, and various industries, safeguarding sensitive information has become imperative. This thesis explores innovative strategies to bolster document security amidst the backdrop of increasing cyber-attacks. Common cybersecurity threats including Phishing Attacks, Ransomware, Supply Chain Attacks, Zero-Day Exploits, Business Email Compromise (BEC), Credential Stuffing, Distributed Denial of Service (DDoS), Insider Threats, Malware Attacks, Advanced Persistent Threats (APTs), and Data Breaches are examined.Data breaches, characterized by unauthorized access, theft, or exposure of confidential information, are particularly concerning. Such breaches can occur through various means, including exploiting system vulnerabilities, social engineering, insider threats, or physical breaches. Notably, techniques like Optical Character Recognition (OCR) pose risks of information extraction without leaving traces. The consequences of a data breach extend beyond mere loss of data. They encompass financial ramifications, damage to reputation, regulatory non-compliance, and operational disruption. To mitigate these risks, this thesis advocates for context-aware document security solutions. It explores the integration of biometric authentication methods and augmented/virtual reality (AR/VR) technology to enhance document security. Specifically, the study delves into AR/VR applications for secure document reading and iris verification within VR environments. Through research and experimentation, the efficacy of FOVIA technology for preventing unauthorized document viewing is unveiled. Additionally, machine learning algorithms are leveraged to dynamically adjust display parameters based on user focus, enhancing security.Hardware modifications, such as utilizing VR/AR headsets for secure document visualization and signing, are explored to address security threats comprehensively. Iris recognition emerges as a robust biometric authentication method, offering stability, resistance to spoofing, and confidentiality considerations. To ensure traceability and integrity, the thesis outlines traceability markers and proposes measures to prevent unauthorized copying, screenshots, and screen recording. It evaluates biometric authentication methods' implementation, emphasizing their effectiveness in enhancing document security while addressing privacy concerns. Experimental findings underscore the effectiveness of document security techniques, particularly in mitigating OCR detection while preserving visual quality. However, challenges remain, as evidenced by the failure of certain techniques in real-world applications, highlighting the need for further research and development.In summary, amidst escalating cyber threats, context-aware document security emerges as a crucial paradigm to safeguard sensitive information effectively. By integrating biometric authentication and AR/VR technologies, organizations can bolster their defenses against evolving cyber threats and mitigate the far-reaching consequences of data breaches.