Effective Management and Performance Improvement Of Network Security Framework Using AI/ML.
Keywords:
technologies, Artificial Intelligence (AI), Machine Learning (ML)Abstract
The integration of virtualization technologies, Artificial Intelligence (AI), and Machine Learning (ML) into network management has transformed traditional network infrastructures, offering enhanced security, flexibility, scalability, and efficiency. This paper explores secure network management and performance improvement using these advanced technologies. We utilize a comprehensive dataset to analyze the impact of virtualization, AI, and ML on network performance and security. By leveraging these technologies, we demonstrate the benefits and challenges of virtualized network environments. The findings are presented through tables and graphs, providing a clear understanding of the improvements achieved.
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Liu, W., Wang, L., & Tan, Y. (2018). Artificial Intelligence for Cybersecurity: A Comprehensive Overview. IEEE Access. This paper provides an extensive overview of how AI is being applied in cybersecurity, highlighting its benefits and challenges.
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2014). Network Anomaly Detection: Methods, Systems and Tools. IEEE Communications Surveys & Tutorials. This survey paper covers various anomaly detection techniques, including those using machine learning, and their application in network security
IBM Security (2019). AI and Machine Learning in Cybersecurity: Opportunities and Challenges. This white paper explores the specific use cases of AI and ML in cybersecurity, detailing the benefits and implementation strategies.
Symantec (2019). The Role of AI and Machine Learning in Strengthening Cybersecurity. This article highlights how Symantec uses AI and ML in their products to enhance threat detection and response.
Buczak, A. L., & Guven, E. (2016). A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2014). Network Anomaly Detection: Methods, Systems and Tools. IEEE Communications Surveys & Tutorials, 16(1), 303-336.
Suh, J. H., et al. (2020). A Survey on Intelligent Intrusion Detection Systems for IoT: A Machine Learning Perspective. IEEE Access, 8, 219201-219222.
Yang, Y., et al. (2014). Survey on Security and Privacy Issues in Internet-of-Things. IEEE Internet of Things Journal, 4(1), 125-153.
Gundu, S. R., Panem, C., &Vijaylaxmi, J. (2023). A Comprehensive Study on Cloud Computing and its Security Protocols and Performance Enhancement Using Artificial Intelligence. Robotic Process Automation, 1-17
Dongre, N., Atique, M., Shaik, Z. A., & Raut, A. D. (2022, January). A survey on security issues and secure frameworks in internet of 6 things (iot). In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 173-181). IEEE
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