OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure
- Title
- OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure
- Creator
- Krishnan P.; Jain K.; Aldweesh A.; Prabu P.; Buyya R.
- Description
- Network Intrusion Detection Systems (NIDS) and firewalls are the de facto solutions in the modern cloud to detect cyberattacks and minimize potential hazards for tenant networks. Most of the existing firewalls, perimeter security, and middlebox solutions are built on static rules/signatures or simple rule matching, making them inflexible, susceptible to bugs, and difficult to introduce new services. This paper aims to improve network management in OpenStack Clouds by taking advantage of the combination of software-defined networking (SDN), Network Function Virtualization (NFV), and machine learning/artificial intelligence (ML/AI) and for making networks more predictable, reliable, and secure. Artificial intelligence is being used to monitor the behavior of the virtual machines and applications running in the OpenStack SDN cloud so that when any issues or degradations are noticed, the decision can be quickly made on how to handle that issue, being able to analyze data in motion, starting at the edge. The OpenStackDP framework comprises lightweight monitoring, anomaly-detecting intelligent sensors embedded in the data plane, a threat analytics engine based on ML/AI algorithms running inside switch hardware/network co-processor, and defensive actions deployed as virtual network functions (VNFs). This network data plane-based architecture makes high-speed threat detection and rapid response possible and enables a much higher degree of security. We have built the framework with advanced streaming analytics technologies, algorithms, and machine learning to draw knowledge from this data that is in motion before the malicious traffic goes to the tenant compute nodes or long-term data store. Cloud providers and users will benefit from improved Quality-of-Services (QoS) and faster recovery from cyber-attacks and compromised switches. The multi-phase collaborative anomaly detection scheme demonstrates an accuracy of 99.81%, average latencies of 0.27 ms, and response speed within 9 s. The simulations and analysis show that the OpenStackDP network analytics framework substantially secures and outperforms prior SDN-based OpenStack solutions for Cloud architectures. 2023, The Author(s).
- Source
- Journal of Cloud Computing, Vol-12, No. 1
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Analytics; Cloud security; Intrusion detection; Machine learning; NFV; OpenStack networking; SDN
- Coverage
- Krishnan P., Center for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri-Campus, Kerala, Kollam, India; Jain K., Center for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri-Campus, Kerala, Kollam, India; Aldweesh A., College of Computing and Information Technology, Shaqra University, Riyadh, 11911, Saudi Arabia; Prabu P., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Buyya R., Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 2192113X
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Krishnan P.; Jain K.; Aldweesh A.; Prabu P.; Buyya R., “OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 27, 2025, https://archives.christuniversity.in/items/show/13947.