Author:
Krishnan Prabhakar,Jain Kurunandan,Aldweesh Amjad,Prabu P.,Buyya Rajkumar
Abstract
AbstractNetwork 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.
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Reference69 articles.
1. Singh S, Jeong YS, Park JH (2016) A survey on cloud computing security: issues, threats, and solutions. J Netw Comput Appl 75:200–222
2. Data breach investigations report (2019) https://www.verizon.com/business/resources/reports/2019-data-breach-investigations-report.pdf. Accessed 24 Feb 2023
3. Jararweh Y, Al-Ayyoub M, Benkhelifa E, Vouk M, Rindos A (2016) Software defined cloud: Survey, system and evaluation. Future Generation Computer Systems 58:56–74
4. McKeown N, Anderson T, Balakrishnan H, Parulkar G, Peterson L, Rexford J, Shenker S, Turner J (2008) “OpenFlow: Enabling Innovation in Campus Networks,” ACM SIGCOMM Computer Communication Review 38(2):69–74. https://doi.org/10.1145/1355734.1355746
5. Open Virtual Network Project. https://www.ovn.org/en/. Accessed 24 Feb 2023
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