AI Based Predictive Analytics To Stop Attacks On Cloud Resources From Cryptocurrencies
Keywords:
cryptojacking, cloud resources, artificial intelligence, cloud securityAbstract
Cryptojacking attacks are a serious threat to the safety of cloud, which are led by hostile users using the cloud computing resources while mining cryptocurrency without authorization. Nowadays, organisations rely more and more on cloud computing for their operations, for that matter, it becomes imperative to avoid or even minimise such attacks. This paper explains the use of artificial intelligence based predictive analytics in this field to predict and halt attacks on cloud resources which are raised from Cryptojacking. AI system detects the odd behaviour, fight probable attack routes and respond quickly to safeguard cloud infrastructure.
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References
Smith, J., & Miller, L. (2023). Leveraging AI for enhanced cloud security: Predictive analytics in cryptojacking prevention. Journal of Cybersecurity Research, 34(2), 45-56.
Brown, T., & Patel, R. (2022). Machine learning models for anomaly detection in cloud resources. Cloud Computing Review, 19(1), 123-134.
Williams, A., & Walker, D. (2023). Real-time threat detection in cloud environments using AI-based predictive analytics. International Journal of Cloud Security, 15(3), 211-225.
Zhang, H., & Chen, Y. (2021). Cryptojacking attacks: A review of detection and prevention methods. Cybersecurity Technologies Journal, 12(4), 78-92.
Li, S., & Wang, X. (2020). Cloud security: The role of machine learning in preventing cryptojacking. Journal of Cloud Computing Security, 7(1), 65-76.
S. Kumari, “Kanban and AI for Efficient Digital Transformation: Optimizing Process Automation, Task Management, and Cross-Departmental Collaboration in Agile Enterprises”, Blockchain Tech. & Distributed Sys., vol. 1, no. 1, pp. 39–56, Mar. 2021
Sivaraman, Hariprasad. (2020). Integrating Large Language Models for Automated Test Case Generation in Complex Systems.
Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-172.
S. Kumari, “Kanban-Driven Digital Transformation for Cloud-Based Platforms: Leveraging AI to Optimize Resource Allocation, Task Prioritization, and Workflow Automation”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 568–586, Jan. 2021
S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019
Singu, Santosh Kumar. "Impact of Data Warehousing on Business Intelligence and Analytics." ESP Journal of Engineering & Technology Advancements 2.2 (2022): 101-113.
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, “Digital Transformation Frameworks for Legacy Enterprises: Integrating AI and Cloud Computing to Revolutionize Business Models and Operational Efficiency ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 186–204, Jan. 2021
Sivaraman, Hariprasad. (2020). Intelligent Deployment Orchestration Using ML for Multi-Environment CI/CD Pipelines.
S. Kumari, “AI-Powered Cybersecurity in Agile Workflows: Enhancing DevSecOps in Cloud-Native Environments through Automated Threat Intelligence ”, J. Sci. Tech., vol. 1, no. 1, pp. 809–828, Dec. 2020.
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.
Sivaraman, Hariprasad. (2021). INTELLIGENT AUTOMATION FOR SERVICE DEGRADATION PREDICTION USING LLMS AND OBSERVABILITY DATA. International Journal of Engineering Management. 6. 10.5281/zenodo.14342920.
S. Kumari, “AI-Powered Cloud Security for Agile Transformation: Leveraging Machine Learning for Threat Detection and Automated Incident Response ”, Distrib Learn Broad Appl Sci Res, vol. 6, pp. 467–488, Oct. 2020
Singu, Santosh Kumar. "ETL Process Automation: Tools and Techniques." ESP Journal of Engineering & Technology Advancements 2.1 (2022): 74-85.
Wang, K., & Zhou, F. (2022). Anomaly detection using AI: A study of cryptojacking prevention strategies. IEEE Cloud Computing Magazine, 18(5), 34-46.
Taylor, C., & Rogers, M. (2023). AI for predictive analytics in cybersecurity: The future of cryptojacking detection. Journal of Artificial Intelligence in Cybersecurity, 11(2), 90-103.
Liu, F., & Zhang, W. (2021). Deep learning approaches to cryptojacking prevention. Journal of Computer Networks and Security, 20(6), 142-157.
Patel, M., & Gupta, R. (2022). The impact of predictive analytics on cloud security. Cloud Systems Journal, 9(3), 101-113.
Choi, Y., & Park, J. (2020). Machine learning for detecting cryptojacking in cloud environments. Cloud Security and Privacy Review, 14(2), 67-79.
Davis, R., & Lee, J. (2023). Cloud resource management: Using AI to mitigate cryptojacking attacks. International Journal of Network Security, 25(4), 180-195.
Lee, S., & Kim, H. (2022). Federated learning in cybersecurity: Opportunities and challenges. Journal of AI Security, 13(1), 122-135.
Wang, Q., & Zhang, L. (2021). Edge computing for cloud security: Preventing cryptojacking with AI. Edge Computing Review, 5(3), 45-58.
Brown, A., & Green, P. (2023). Using AI for real-time cryptojacking detection. Journal of Cloud Computing Security, 19(1), 23-36.
Morgan, E., & Huang, F. (2022). Enhancing cloud security with predictive AI systems. Journal of Network Security, 27(2), 98-112.