Real-Time Anomaly Detection Using DBSCAN Clustering in Cloud Network Infrastructures
DOI:
https://doi.org/10.36676/jrps.v11.i4.1591Keywords:
Real-time anomaly detection, DBSCAN clustering, cloud network infrastructures, security threats, performance monitoring, high-dimensional data, density-based clusteringAbstract
In the era of cloud computing, ensuring the security and reliability of network infrastructures is paramount. This study presents a novel approach for real-time anomaly detection using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, tailored specifically for cloud network environments. Traditional anomaly detection methods often struggle with high-dimensional data and varying data distributions typical of cloud infrastructures. By leveraging DBSCAN's ability to identify clusters of varying shapes and sizes while effectively handling noise, this research aims to enhance the detection of irregular patterns that may signify potential security threats or performance issues. The proposed system continuously monitors network traffic, applying DBSCAN to dynamically cluster data points and flag anomalies based on density variations. Preliminary results indicate a significant improvement in detection rates compared to conventional methods, showcasing the efficacy of DBSCAN in real-time scenarios. This research contributes to the ongoing development of robust security frameworks for cloud networks, facilitating proactive responses to anomalies and enhancing overall system integrity.
References
Ali, W., Awan, I. A., & Khan, M. A. (2017). A hybrid approach for anomaly detection in network traffic using statistical methods and clustering techniques. Journal of Network and Computer Applications, 89, 1-12. https://doi.org/10.1016/j.jnca.2017.01.004 DOI: https://doi.org/10.1016/j.jnca.2017.01.004
Chen, Y., Zhang, Y., & Xu, L. (2020). An adaptive DBSCAN clustering algorithm for real-time anomaly detection in cloud environments. Future Generation Computer Systems, 112, 1-10. https://doi.org/10.1016/j.future.2020.05.014 DOI: https://doi.org/10.1016/j.future.2020.05.014
García, J., Saiz, A., & Ramírez, J. (2017). A hybrid model for anomaly detection in cloud environments. International Journal of Information Security, 16(3), 215-227. https://doi.org/10.1007/s10207-016-0324-5
Kim, S., Kwon, T., & Park, H. (2019). Enhancing the accuracy of anomaly detection in cloud computing environments through a hybrid approach using DBSCAN and deep learning. IEEE Access, 7, 90809-90820. https://doi.org/10.1109/ACCESS.2019.2924016 DOI: https://doi.org/10.1109/ACCESS.2019.2924016
Li, J., Zhang, H., & Zhou, Z. (2018). Real-time anomaly detection based on DBSCAN clustering for cloud computing. Proceedings of the International Conference on Cloud Computing and Big Data Analytics, 16-23. https://doi.org/10.1109/ICCCBDA.2018.8371772
Nguyen, H. T., Nguyen, D. C., & Huynh, T. D. (2020). A novel ensemble learning method combining DBSCAN and decision trees for anomaly detection in cloud services. Journal of Cloud Computing: Advances, Systems and Applications, 9(1), 1-14. https://doi.org/10.1186/s13677-020-00167-2
Wang, S., Liu, Y., & Zhang, J. (2018). Scalable anomaly detection based on density clustering in cloud computing environments. Journal of Systems and Software, 135, 197-206. https://doi.org/10.1016/j.jss.2017.11.050 DOI: https://doi.org/10.1016/j.jss.2017.11.050
Zhang, Y., Li, H., & Xu, L. (2019). A comparative study of clustering algorithms for network anomaly detection in hybrid cloud environments. Information Sciences, 480, 138-150. https://doi.org/10.1016/j.ins.2018.12.054 DOI: https://doi.org/10.1016/j.ins.2018.12.016
Goel, P. & Singh, S. P. (2009). Method and Process Labor Resource Management System. International Journal of Information Technology, 2(2), 506-512.
Singh, S. P. & Goel, P., (2010). Method and process to motivate the employee at performance appraisal system. International Journal of Computer Science & Communication, 1(2), 127-130.
Goel, P. (2012). Assessment of HR development framework. International Research Journal of Management Sociology & Humanities, 3(1), Article A1014348. https://doi.org/10.32804/irjmsh DOI: https://doi.org/10.32804/IRJMSH
Goel, P. (2016). Corporate world and gender discrimination. International Journal of Trends in Commerce and Economics, 3(6). Adhunik Institute of Productivity Management and Research, Ghaziabad.
Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. https://rjpn.org/ijcspub/papers/IJCSP20B1006.pdf
"Effective Strategies for Building Parallel and Distributed Systems", International Journal of Novel Research and Development, ISSN:2456-4184, Vol.5, Issue 1, page no.23-42, January-2020. http://www.ijnrd.org/papers/IJNRD2001005.pdf
"Enhancements in SAP Project Systems (PS) for the Healthcare Industry: Challenges and Solutions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 9, page no.96-108, September-2020, https://www.jetir.org/papers/JETIR2009478.pdf
Venkata Ramanaiah Chintha, Priyanshi, Prof.(Dr) Sangeet Vashishtha, "5G Networks: Optimization of Massive MIMO", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 1, Page No pp.389-406, February-2020. (http://www.ijrar.org/IJRAR19S1815.pdf )
Cherukuri, H., Pandey, P., & Siddharth, E. (2020). Containerized data analytics solutions in on-premise financial services. International Journal of Research and Analytical Reviews (IJRAR), 7(3), 481-491 https://www.ijrar.org/papers/IJRAR19D5684.pdf
Sumit Shekhar, SHALU JAIN, DR. POORNIMA TYAGI, "Advanced Strategies for Cloud Security and Compliance: A Comparative Study", IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, Volume.7, Issue 1, Page No pp.396-407, January 2020. (http://www.ijrar.org/IJRAR19S1816.pdf )
"Comparative Analysis OF GRPC VS. ZeroMQ for Fast Communication", International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 2, page no.937-951, February-2020. (http://www.jetir.org/papers/JETIR2002540.pdf )
Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. https://rjpn.org/ijcspub/papers/IJCSP20B1006.pdf
"Effective Strategies for Building Parallel and Distributed Systems". International Journal of Novel Research and Development, Vol.5, Issue 1, page no.23-42, January 2020. http://www.ijnrd.org/papers/IJNRD2001005.pdf
"Enhancements in SAP Project Systems (PS) for the Healthcare Industry: Challenges and Solutions". International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 9, page no.96-108, September 2020. https://www.jetir.org/papers/JETIR2009478.pdf
Venkata Ramanaiah Chintha, Priyanshi, & Prof.(Dr) Sangeet Vashishtha (2020). "5G Networks: Optimization of Massive MIMO". International Journal of Research and Analytical Reviews (IJRAR), Volume.7, Issue 1, Page No pp.389-406, February 2020. (http://www.ijrar.org/IJRAR19S1815.pdf)
Cherukuri, H., Pandey, P., & Siddharth, E. (2020). Containerized data analytics solutions in on-premise financial services. International Journal of Research and Analytical Reviews (IJRAR), 7(3), 481-491. https://www.ijrar.org/papers/IJRAR19D5684.pdf
Sumit Shekhar, Shalu Jain, & Dr. Poornima Tyagi. "Advanced Strategies for Cloud Security and Compliance: A Comparative Study". International Journal of Research and Analytical Reviews (IJRAR), Volume.7, Issue 1, Page No pp.396-407, January 2020. (http://www.ijrar.org/IJRAR19S1816.pdf)
"Comparative Analysis of GRPC vs. ZeroMQ for Fast Communication". International Journal of Emerging Technologies and Innovative Research, Vol.7, Issue 2, page no.937-951, February 2020. (http://www.jetir.org/papers/JETIR2002540.pdf)
Eeti, E. S., Jain, E. A., & Goel, P. (2020). Implementing data quality checks in ETL pipelines: Best practices and tools. International Journal of Computer Science and Information Technology, 10(1), 31-42. Available at: http://www.ijcspub/papers/IJCSP20B1006.pdf
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