Security Assessment of a Machine Learning Approach to Generate and Validate Digital Signatures

<p>Cybersecurity has become a global concern as cyber-attacks have become more common, and the cost of the damage caused by them continues to increase. There are several approaches to improve the cyber security of systems such as Digital Signatures, hashing, watermarking, and encryption among others. Digital Signatures are a cryptographic technique used to verify the authenticity and integrity of digital messages or documents. Digital Signatures use a combination of hashing and public-private key encryption to verify the authenticity and integrity of videos, just as they are used for documents and messages. As a result of using a combination of other methods, Digital Signatures inherit the weaknesses from the hashing algorithm used, which include collisions, preimage resistance, or poor performance. specializes in enabling systems to learn from data and improve performance over time without being explicitly programmed. Machine Learning algorithms leverage statistical techniques to identify patterns and make predictions or decisions based on historical data. Unlike traditional software that follows a fixed set of rules, Machine Learning models adapt and refine their predictions as they are exposed to more data. The goal of this research is to show that Machine Learning can be used to generate Digital Signatures and be as successful as current methods for Digital Signature generation and verification to make more resilient and robust methods. The hashing section of the Digital Signature would be replaced by a Machine Learning model which will hash the message sent. The performance of the new Digital Signature process will be compared to regular Digital Signature methods using time to sign, time to verify, and resource usage (RAM and memory). We posit that Machine Learning generated Digital Signatures can have comparable performance to traditionally generated Digital Signatures while potentially increasing security.</p>

Name thesis:degree_name

Doctor of Philosophy in Electrical Engineering & Computer Science

Level thesis:degree_level

Dissertation - Open Access

Discipline thesis:degree_discipline

Electrical, Computer, Software, and Systems Engineering

Year

2026

Author dc:creator

• Ortiz Couder, Juan

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• Hashing
• Digital Signatures
• Artificial Intelligence and Robotics
• Information Security

Repository record dc:identifier

https://commons.erau.edu/edt/986

OAI identifier oai:identifier

oai:commons.erau.edu:edt-2035