Blockchain-Powered Graph-Based AI Models Predict Real-Time Cyberattacks
Keywords:
cyberattack prediction, blockchain security, graph-based AIAbstract
The usage of blockchain in important businesses has increased the demand for powerful cyberdefenses. AI and graph-based algorithms in blockchain-powered systems may enhance real-time danger prediction. Graph-based AI algorithms may identify complex blockchain network patterns and relationships, identifying suspicious activities and threats. This article discusses graph-based AI models, blockchain application, and real-time cyberattack prediction benefits and downsides. This paper analyzes current research and case examples to demonstrate how these models might speed up threat detection and mitigation in cybersecurity frameworks. Effective data administration, model interpretability, and integration are needed to exploit these promising models.
Downloads
References
Madupati, Bhanuprakash. "Integration of Cloud Computing in Smart Cities: Opportunities, Challenges, and Future Direction Paper." Challenges, and Future Direction Paper (December 06, 2019) (2019).
Gupta, Neha, and Vivek Kapoor. "Hybrid cryptographic technique to secure data in web application." Journal of Discrete Mathematical Sciences and Cryptography 23.1 (2020): 125-135.
Pillai, Vinayak. Anomaly Detection for Innovators: Transforming Data into Breakthroughs. Libertatem Media Private Limited, 2022.
Talati, Dhruvitkumar V. "Silicon minds: The rise of AI-powered chips." (2021).
Kalluri, Kartheek. "Migrating Legacy System to Pega Rules Process Commander v7. 1." (2015).
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
Madupati, Bhanuprakash. "Revolution of Cloud Technology in Software Development." Available at SSRN 5146576 (2019).
Gondaliya, Jayraj, et al. "Hybrid security RSA algorithm in application of web service." 2018 1st International Conference on Data Intelligence and Security (ICDIS). IEEE, 2018.
Talati, Dhruvitkumar. "Artificial Intelligence and unintended bias: A call for responsible innovation." (2021).
Kalluri, Kartheek. "ENHANCING CUSTOMER SERVICE EFFICIENCY: A COMPARATIVE STUDY OF PEGA'S AI-DRIVEN SOLUTIONS."
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
Madupati, Bhanuprakash. "Blockchain in Day-to-Day Life: Transformative Applications and Implementation." Available at SSRN 5118207 (2021).
Kalluri, Kartheek. "Federate Machine Learning: A Secure Paradigm for Collaborative AI in Privacy-Sensitive Domains." International Journal on Science and Technolo-gy 13.4 (2022): 1-13.
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
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
Kalluri, Kartheek. "Blockchain Augment AI: Securing Decision Pipelines Decentralized in Systems."
Madupati, Bhanuprakash. "Kubernetes: Advanced Deployment Strategies-* Technical Perspective." (2021).
Kalluri, Kartheek. "Optimizing Financial Services Implementing Pega's Decisioning Capabilities for Fraud Detection." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 10.1 (2022): 1-9.
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
