IoT Cybersecurity Neural Architecture Protocol Improvement
Keywords:
Neural Architecture Search, IoT security, adaptive defense mechanisms, automated designAbstract
Smart cities require number of IOT devices, as the number of device increases making secure link is becoming difficult. Traditional cybersecurity struggles with IOT devices in various capabilities and risk. IOT cyber security can be boosted by neural architecture search which will result in agility, efficiency, and resilience. NES automates deep learning model to create an optimised security based on real time network, threat, and device circumstances. This article aims to discuss how NAS-based IoT cybersecurity protocol protect smart cities and its linked devices.
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References
Smith, J., & Xu, L. (2020). Optimizing cybersecurity protocols in IoT networks with NAS techniques. Journal of Applied AI, 14(6), 98-112.
Zhao, S., & Yang, Z. (2021). Adaptive deep learning models for security in IoT environments. Security and Privacy, 4(5), 67-84.
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.
Patel, V., & Gupta, P. (2020). Deep learning in IoT security