Enhancing Cyber Resilience by Integrating AI-Driven Threat Detection and Mitigation Strategies

Dr. Vinod Varma Vegesna

Abstract


This paper delves into the synergy between artificial intelligence (AI) and cybersecurity, exploring the evolving landscape of threats and vulnerabilities in the digital realm. It investigates the application of AI-driven methodologies for bolstering cyber resilience, emphasizing the detection and mitigation of sophisticated cyber threats. The study evaluates various AI models, algorithms, and technologies utilized in threat identification, response, and recovery processes. Furthermore, it assesses the effectiveness of AI-integrated systems in adapting to dynamic cyber threats, emphasizing their role in fortifying the security posture of organizations and networks. This research aims to provide insights into the transformative potential of AI in enhancing cyber resilience and mitigating emerging cyber risks.

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References


Aickelin, U., & Das, S. (2011). Artificial intelligence for security. Springer Science & Business Media.

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.

Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662-679.

Choo, K. K. R. (2011). Cybercrime: The challenge for the twenty-first century. International Journal of Cyber Criminology, 5(1), 827-841.

Ghosh, A., Swaminathan, R., & Utkarshani, J. (2020). Machine Learning and Security: Protecting Systems with Data and Algorithms. CRC Press.

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399.

McWhorter, J. R., & Draelos, T. M. (2018). Machine learning in cybersecurity. CRC Press.

Mittal, S., Raj, H., & Aickelin, U. (2018). A review of machine learning approaches for cyber security analytics. In Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security (pp. 819-824).

Samon, J., Carney, D., & Liu, Y. (2021). AI-Enabled Cyber Defense Systems: Next Generation Implementation and Deployment. Springer.

Scarfone, K., & Mell, P. (2007). Guide to intrusion detection and prevention systems (IDPS). National Institute of Standards and Technology.

Wang, S., Li, Q., & Ma, J. (2021). Deep Learning for Cybersecurity: Attack and Defense Mechanisms. John Wiley & Sons.

Zhang, H., Liu, S., & Zhang, W. (2020). Cybersecurity challenges and opportunities: AI in the loop. Future Generation Computer Systems, 107, 1037-1048.

Bens, R. E. (2015). Cybersecurity and artificial intelligence: A way forward. Harvard National Security Journal, 6, 457-469.

Bojanova, I., Scholl, M., & Venter, H. (Eds.). (2018). Artificial Intelligence and Cybersecurity: A Primer. CRC Press.

Peddireddy, K. (2023, October 20). Effective Usage of Machine Learning in Aero Engine test data using IoT based data driven predictive analysis. IJARCCE, 12(10).

https://doi.org/10.17148/ijarcce.2023.121003

Peddireddy, A., & Peddireddy, K. (2023, March 30). Next-Gen CRM Sales and Lead Generation with AI. International Journal of Computer Trends and Technology, 71(3), 21–26. https://doi.org/10.14445/22312803/ijctt-v71i3p104

Ghosh, D., & Irani, D. (2016). A survey of machine learning algorithms for big data analytics. Journal of Big Data, 3(1), 1-32.

Peddireddy, K. (2023, May 11). Streamlining Enterprise Data Processing, Reporting and Realtime Alerting using Apache Kafka. 2023 11th International Symposium on Digital Forensics and Security (ISDFS). https://doi.org/10.1109/isdfs58141.2023.10131800.

Martellini, M., & Rule, S. (2016). Cybersecurity: The Insights You Need from Harvard Business Review. Harvard Business Review Press.

Peddireddy, K. (2023, May 18). Kafka-based Architecture in Building Data Lakes for Real-time Data Streams. International Journal of Computer Applications, 185(9), 1–3. https://doi.org/10.5120/ijca2023922740

Atluri, H., & Thummisetti, B. S. P. (2023). Optimizing Revenue Cycle Management in Healthcare: A Comprehensive Analysis of the Charge Navigator System. International Numeric Journal of Machine Learning and Robots, 7(7), 1-13.

Atluri, H., & Thummisetti, B. S. P. (2022). A Holistic Examination of Patient Outcomes, Healthcare Accessibility, and Technological Integration in Remote Healthcare Delivery. Transactions on Latest Trends in Health Sector, 14(14).


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