Adaptive Threat Mitigation Frameworks Using AI in Energy-Efficient Edge Computing Systems
Keywords:
edge computing, adaptive threat mitigation, artificial intelligence, machine learningAbstract
As edge computing continues to emerge as a key technology for distributed systems, the need for efficient and adaptive threat mitigation frameworks becomes crucial, especially in the context of energy-efficient systems. Edge computing’s architecture, which involves the decentralization of computation to closer locations to end-users, introduces unique challenges in terms of security, particularly regarding the increasing frequency and complexity of cyber threats. This paper explores the integration of artificial intelligence (AI) into adaptive threat mitigation frameworks within energy-efficient edge computing systems. It discusses the benefits of AI-driven security solutions, which can dynamically detect and respond to threats while ensuring minimal energy consumption. Through the application of machine learning, deep learning, and reinforcement learning algorithms, AI can be leveraged to enhance threat detection capabilities, automate responses, and optimize energy usage. The paper further investigates the practical implications of implementing these frameworks, highlighting case studies and identifying challenges such as resource constraints and system scalability. Ultimately, it presents the future potential of adaptive AI models in safeguarding energy-efficient edge computing systems while preserving operational efficiency.
Downloads
References
Choi, B., & Kim, M. (2020). Anomaly detection in IoT networks using machine learning algorithms. IEEE Transactions on Industrial Informatics, 16(9), 2303-2311.
Wang, L., & Zhang, Y. (2019). Deep learning for cybersecurity in edge computing systems. International Journal of Computer Science and Network Security, 19(7), 45-52.
Xu, H., & Liu, Z. (2018). Reinforcement learning for adaptive security in distributed systems. Journal of Cyber Security and Privacy, 5(2), 100-115.
Zhao, W., & Li, Y. (2021). Optimizing energy efficiency in machine learning models for IoT networks. IEEE Transactions on Green Communications and Networking, 5(4), 1102-1113.
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
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.
Madupati, Bhanuprakash. "Blockchain in Day-to-Day Life: Transformative Applications and Implementation." Available at SSRN 5118207 (2021).
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
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 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
Madupati, Bhanuprakash. "Kubernetes: Advanced Deployment Strategies-* Technical Perspective." (2021).
Zhang, F., & Li, T. (2021). Optimizing energy consumption in AI models for IoT security. IEEE Transactions on Energy-efficient Computing, 10(4), 214-225.
Huang, J., & Zhang, L. (2021). Machine learning techniques for edge device security. IEEE Transactions on Industrial Electronics, 14(8), 3211-3225.
Wang, Z., & Wu, J. (2018). AI in IoT cybersecurity: Challenges and solutions. IEEE Access, 6, 11634-11645.
Li, H., & Zhou, Z. (2020). Privacy-preserving reinforcement learning for edge computing. Journal of Machine Learning Research, 21(7), 136-147.
Lee, D., & Kim, T. (2020). Resource-constrained AI models for IoT security. Proceedings of the IEEE International Conference on Smart Grid Communications, 100-107.
Li, M., & Zhang, P. (2019). Adaptive threat response using machine learning. IEEE Transactions on Network and Service Management, 18(3), 1493-1506.
Zhao, L., & Yao, Y. (2018). Federated learning in energy-efficient edge systems. International Journal of Edge Computing, 7(3), 45-58.
Wang, X., & Chen, W. (2020). Optimizing security protocols for energy-efficient edge computing. IEEE Transactions on Cloud Computing, 8(6), 1153-1167.
Li, J., & Liu, T. (2021). Adaptive machine learning algorithms for energy-efficient edge security. Journal of Distributed Computing, 13(4), 102-115.
