ADAPTIVE CYBERSECURITY STRATEGIES FOR CLOUD-BASED REAL-TIME DATA ANALYTICS PLATFORMS
Keywords:
Adaptive Cybersecurity, Cloud-Based Real-Time Analytics, AI-Driven Threat Detection, Zero-Trust Architecture, Behavioral AnalyticsAbstract
This article explores the critical intersection of adaptive cybersecurity strategies and cloud-based real-time data analytics platforms, addressing the unique security challenges posed by these dynamic environments. The article presents a comprehensive framework that leverages artificial intelligence, machine learning, and behavioral analytics to create a robust, responsive security infrastructure capable of evolving alongside emerging threats. By integrating zero-trust principles, automated incident response mechanisms, and continuous compliance monitoring, the proposed framework offers a holistic environment. The article examines the multifaceted benefits of cloud computing for real-time analytics, including scalability, cost-efficiency, and global reach, while emphasizing the importance of balancing these advantages with stringent security measures. Through an analysis of current cybersecurity limitations and the potential of adaptive strategies, this article provides valuable insights for organizations seeking to harness the power of cloud-based real-time analytics while maintaining a strong security posture in an increasingly complex threat landscape. The findings underscore the necessity of adopting dynamic, intelligent security frameworks to protect critical data assets and ensure the integrity of the real-time approaches to safeguarding sensitive data and analytics processes in distributed cloud analytics operations in the cloud era.
References
P. Mell and T. Grance, "The NIST Definition of Cloud Computing," National Institute of Standards and Technology, Gaithersburg, MD, USA, Special Publication 800-145, Sep. 2011. [Online]. Available: https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf
A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, "Survey of intrusion detection systems: techniques, datasets and challenges," Cybersecurity, vol. 2, no. 1, p. 20, Jul. 2019. [Online]. Available: https://cybersecurity.springeropen.com/articles/10.1186/s42400-019-0038-7
C. Esposito, A. Castiglione, B. Martini, and K.-K. R. Choo, "Cloud Manufacturing: Security, Privacy, and Forensic Concerns," IEEE Cloud Computing, vol. 3, no. 4, pp. 16-22, July-Aug. 2016. [Online]. Available: https://ieeexplore.ieee.org/document/7571085
M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, "A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security," IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1646-1685, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9072101
Abdulla Amin Aburomman, Mamun Bin Ibne Reaz, A survey of intrusion detection systems based on ensemble and hybrid classifiers, Computers & Security, Volume 65, 2017, Pages 135-152, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2016.11.004
A. Botta, W. de Donato, V. Persico, and A. Pescapé, "Integration of Cloud computing and Internet of Things: A survey," Future Generation Computer Systems, vol. 56, pp. 684-700, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0167739X15003015
C. Modi, D. Patel, B. Borisaniya, A. Patel, and M. Rajarajan, "A survey on security issues and solutions at different layers of Cloud computing," The Journal of Supercomputing, vol. 63, no. 2, pp. 561-592, 2013. [Online]. Available: https://link.springer.com/article/10.1007/s11227-012-0831-5
