The Role of Artificial Intelligence in Website Encryption
DOI:
https://doi.org/10.59992/IJCI.2025.v4n2p1Keywords:
Artificial Intelligence, Encryption, Cybersecurity, Machine Learning, Smart Encryption, Threat Detection, Security ProtocolsAbstract
The rapid advancement of digital services and the increasing reliance on internet-based transactions have highlighted the crucial need for robust cybersecurity measures. Encryption is a fundamental technique used to secure sensitive data, preventing unauthorized access and data manipulation. However, traditional encryption methods face limitations in adapting to evolving cyber threats. Artificial Intelligence (AI) has emerged as a transformative force in enhancing encryption techniques, leveraging deep learning, neural networks, and predictive analytics to provide dynamic and adaptive encryption solutions. This paper explores the role of AI in website encryption, focusing on its contributions to smart encryption, real-time threat detection, and the enhancement of encryption protocols such as TLS and SSL. Additionally, the study examines the challenges associated with AI-driven encryption, including high computational resource requirements, the risks of AI-powered attacks, and implementation complexities. As AI continues to evolve, its integration into encryption methodologies is expected to play a pivotal role in strengthening cybersecurity frameworks and mitigating emerging cyber threats.
References
1. Smith, J., & Brown, K. (2022). Artificial Intelligence in Cybersecurity. Journal of AI Research, 45(3), 123-135.
2. Johnson, L. (2021). Machine Learning for Secure Encryption. Cybersecurity Advances, 12(1), 45-60.
3. Patel, R. (2020). Enhancing Cryptographic Methods with AI. International Journal of Encryption Science, 8(4), 220-234.
4. White, M. & Green, D. (2022). The Role of AI in Threat Detection. Computing Security Review, 19(2), 98-115.
5. Williams, H. (2019). Adversarial Attacks on AI Models. Cyber Defense Journal, 27(5), 30-50.
6. Zhang, X. & Lee, T. (2021). AI-driven Key Management Systems. Cryptographic Studies, 17(1), 55-72.
7. Miller, C. (2020). The Evolution of Post-Quantum Cryptography. Quantum Computing Review, 5(6), 67-85.
8. Garcia, F. & Jones, R. (2022). AI-Based Adaptive Security Measures. Cyber Intelligence, 14(3), 101-120.
9. Ahmed, S. (2023). AI and Network Security. Digital Protection Review, 11(2), 76-92.
10. Kim, Y. (2022). AI in Secure Communications. Wireless Security Journal, 23(4), 203-219.
11. Thomas, N. (2021). Preventing MITM Attacks with AI. Network Security Review, 9(3), 40-58.
12. Rogers, P. (2022). AI and Blockchain for Encryption. Blockchain Security Analysis, 7(1), 88-105.
13. Singh, A. (2020). AI-Based Encryption Algorithms. Cryptology Advances, 18(4), 99-120.
14. Hassan, M. (2019). The Future of Cybersecurity with AI. Global Security Studies, 10(3), 65-78.
15. Davidson, J. & Lewis, K. (2021). AI in Financial Data Security. Financial Tech Review, 5(2), 111-130.
16. Murphy, R. (2023). AI and Cloud Security Encryption. Cloud Computing Security Journal, 8(5), 45-63.
17. Cohen, D. (2021). Ethical Implications of AI in Cybersecurity. Ethics in AI Research, 6(1), 12-30.
18. Wang, Z. (2020). AI-Enhanced Intrusion Detection Systems. Cyber Defense Science, 15(2), 92-108.
19. Evans, L. & Carter, S. (2022). AI’s Role in Identity Protection. Data Privacy and Security Review, 21(4), 147-165.
20. Nelson, G. (2023). AI for Real-Time Encryption. Digital Security Research, 14(3), 78-95.
