Intelligent Threat Hunting: A Systematic Literature Review and Meta-Analysis of the RSA Cryptosystem
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The RSA cryptosystem has received considerable acceptance because of its ability to withstand numerous attacks across diverse applications, and protecting digital information is essential. Historical and contemporary approaches to analyzing its algebraic security structure are essentially rooted in mathematical frameworks. Cryptanalysis with deep learning (DL) has become viable because of advancements in computing resources. However, this niche lacks systematic reviews and meta-analyses on DL-based cryptanalysis. This study marks the initial instance of a systematic review applying DL models to assess the structural challenges inherent in the RSA cryptosystem. The study presents a comprehensive analysis of 65 research papers covering five key RSA problem areas: factorization attack, weak private exponent attack, partial key exposure attack, key equation attack, and encryption scheme attack. The literature was assessed through quantitative and qualitative approaches via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for reporting. Through the examination carried out in this study, we develop a detailed research roadmap of previous studies on classical techniques applied to cryptanalysis operations with a specific focus on the RSA algebraic structure, along with the recognition of current challenges and recommended strategies for the utilization of DL methods in cryptanalysis tasks.
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