Quantum communication network security AI models
Keywords:
artificial intelligence, quantum communication, post-quantum cryptography, quantum securityAbstract
Quantum computing is a big threat to the present cryptographic protocols and is very necessary for post quantum cryptography. Quantum communication networks are becoming vital option for secure information transit as security is very crucial. To improve the network security AI models can be used to improve resilience in this situation. This research examines the PQC and intelligent AI models are able to protect quantum communication networks or not.
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References
Chang, H., & Zhang, W. (2023). Post-quantum cryptography and AI: A new paradigm for secure communications. IEEE Transactions on Quantum Communication, 17(1), 91-105.
S. Kumari, “AI-Driven Cloud Transformation for Product Management: Optimizing Resource Allocation, Cost Management, and Market Adaptation in Digital Products ”, IoT and Edge Comp. J, vol. 2, no. 1, pp. 29–54, Jun. 2022
Pillai, Vinayak. “Implementing Efficient Data Operations: An Innovative Approach”. Asian Journal of Multidisciplinary Research & Review, vol. 3, no. 6, Dec. 2022, pp. 231-67, https://ajmrr.org/journal/article/view/241.
S. Kumari, “Agile Cloud Transformation in Enterprise Systems: Integrating AI for Continuous Improvement, Risk Management, and Scalability”, Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, pp. 416–440, Mar. 2022
Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.
Sangaraju, Varun Varma, and Senthilkumar Rajagopal. "Applications of Computational Models in OCD." In Nutrition and Obsessive-Compulsive Disorder, pp. 26-35. CRC Press.
Sivaraman, Hariprasad. "Self-Healing Test Automation Frameworks Using Reinforcement Learning for Full-Stack Test Automation." Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-E210. DOI: doi. org/10.47363/JAICC/2022 (1) E210 J Arti Inte & Cloud Comp 1.4 (2022): 2-4.
Singu, Santosh Kumar. "Impact of Data Warehousing on Business Intelligence and Analytics." ESP Journal of Engineering & Technology Advancements 2.2 (2022): 101-113.
S. Kumari, “AI-Enhanced Agile Development for Digital Product Management: Leveraging Data-Driven Insights for Iterative Improvement and Market Adaptation”, Adv. in Deep Learning Techniques, vol. 2, no. 1, pp. 49–68, Mar. 2022
S. Kumari, “AI-Driven Cybersecurity in Agile Cloud Transformation: Leveraging Machine Learning to Automate Threat Detection, Vulnerability Management, and Incident Response”, J. of Art. Int. Research, vol. 2, no. 1, pp. 286–305, Apr. 2022
Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.
Sivaraman, Hariprasad. (2022). Adaptive Thresholding in ML-Driven Alerting Systems for Reducing False Positives in Production Environments. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT. 6. 10.55041/IJSREM11938.
S. Kumari, “Cybersecurity in Digital Transformation: Using AI to Automate Threat Detection and Response in Multi-Cloud Infrastructures ”, J. Computational Intel. & Robotics, vol. 2, no. 2, pp. 9–27, Aug. 2022
Sivaraman, Hariprasad. "Behavior-Based DDoS Detection for Multi-Vector Attacks in Hybrid Cloud Environments." Sivaraman H. Behavior-Based DDoS Detection for Multi-Vector Attacks in Hybrid Cloud Environments.
Anderson, T., & Lee, D. (2021). Hybrid quantum-classical systems in post-quantum cryptography. Quantum Computing and Cryptography, 11(2), 37-49.
