RSAnalyzer: Cryptanalyzing RSA using Supervised Machine Learning Methods

Crowe, Gray (2025) RSAnalyzer: Cryptanalyzing RSA using Supervised Machine Learning Methods. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (371kB) | Preview

Abstract

This work aimed to explore the cross-section of cryptanalysis and supervised machine learning, to determine whether machine learning techniques can be used to detect weaknesses in asymmetric cryptosystems, RSA in this case. RSA was chosen for this research as it is the most widely used asymmetric cryptosystem in the world. Its position as a commonly used cryptosystem in e-commerce transactions highlight the need to use all tools available to analyse and break it.

This research aimed to show supervised machine learning methods can be used to break RSA ciphertext not only in theory but also in practice by using the findings of this research to create a software artefact called RSAnalyzer. The aim of this tool is taking a ciphertext and public key as input, and output the original plaintext based on the weakness found in the ciphertext.

The detection of the weakness will be performed by machine learning models trained to detect one of 4 weakness in RSA’s parameter selection process. Evaluation of the supervised models showed a strong indication that supervised machine learning techniques can reliably detect weaknesses in RSA.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Monaghan, Mark UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security H Social Sciences > HG Finance > Money > Digital currency Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	15 Jun 2026 13:01
Last Modified:	15 Jun 2026 13:01
URI:	https://norma.ncirl.ie/id/eprint/9350

Actions (login required)

View Item