OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure

被引:15
作者
Krishnan, Prabhakar [1 ]
Jain, Kurunandan [1 ]
Aldweesh, Amjad [2 ]
Prabu, P. [3 ]
Buyya, Rajkumar [4 ]
机构
[1] Amrita Vishwa Vidyapeetham, Ctr Cybersecur Syst & Networks, Amritapuri Campus, Kollam, Kerala, India
[2] Shaqra Univ, Coll Comp & Informat Technol, Riyadh 11911, Saudi Arabia
[3] Christ Univ, Dept Comp Sci, Bengaluru, Karnataka, India
[4] Univ Melbourne, Sch Comp & Informat Syst, Cloud Comp & Distributed Syst CLOUDS Lab, Melbourne, Australia
来源
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS | 2023年 / 12卷 / 01期
关键词
SDN; NFV; OpenStack networking; Cloud security; Intrusion detection; Machine learning; Analytics; SOFTWARE; ATTACKS; SYSTEM;
D O I
10.1186/s13677-023-00406-w
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
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页数:42
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