Document Type
Article
Publication Title
International Journal of Advanced Computer Science and Applications
Abstract
The increasing integration of artificial intelligence (AI) within cybersecurity has necessitated stronger encryption methods to ensure data security. This paper presents a comparative analysis of symmetric (SE) and asymmetric encryption (AE) algorithms, focusing on their role in securing sensitive information in AI-driven environments. Through an in-depth study of various encryption algorithms such as AES, RSA, and others, this research evaluates the efficiency, complexity, and security of these algorithms within modern cybersecurity frameworks. Utilizing both qualitative and quantitative analysis, this research explores the historical evolution of encryption algorithms and their growing relevance in AI applications. The comparison of SE and AE algorithms focuses on key factors such as processing speed, scalability, and security resilience in the face of evolving threats. Special attention is given to how these algorithms are integrated into AI systems and how they manage the challenges posed by large-scale data processing in multi-agent environments. Our results highlight that while SE algorithms demonstrate high-speed performance and lower computational demands, AE algorithms provide superior security, particularly in scenarios requiring enhanced encryption for AI-based networks. The paper concludes by addressing the security concerns that encryption algorithms must tackle in the age of AI and outlines future research directions aimed at enhancing encryption techniques for cybersecurity.
DOI
10.14569/IJACSA.2024.0151201
Publication Date
12-2024
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Kshetri, Naresh; Rahman, Mir Mehedi; Rana, MD Masud; Osama, Omar Faruq; and Hutson, James, "algoTRIC: Symmetric and Asymmetric Encryption Algorithms for Cryptography – A Comparative Analysis in AI Era" (2024). Faculty Scholarship. 710.
https://digitalcommons.lindenwood.edu/faculty-research-papers/710
