Journal
SENSORS
Volume 21, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/s21092972
Keywords
software-defined network; flow features; data mining; flow classification; Mininet; OpenDaylight
Funding
- National Centre for Research and Development [CYBERSECIDENT/369195/I/NCBR/2017]
- NASK-National research Institute
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This paper proposes a system that uses SDN features and data mining techniques to protect SDNs against malicious flows, with experiments confirming the system's effectiveness.
The increasing availability of mobile devices and applications, the progress in virtualisation technologies, and advances in the development of cloud-based distributed data centres have significantly stimulated the growing interest in the use of software-defined networks (SDNs) for both wired and wireless applications. Standards-based software abstraction between the network control plane and the underlying data forwarding plane, including both physical and virtual devices, provides an opportunity to significantly increase network security. In this paper, to secure SDNs against intruders' actions, we propose a comprehensive system that exploits the advantages of SDNs' native features and implements data mining to detect and classify malicious flows in the SDN data plane. The architecture of the system and its mechanisms are described, with an emphasis on flow rule generation and flow classification. The concept was verified in the SDN testbed environment that reflects typical SDN flows. The experiments confirmed that the system can be successfully implemented in SDNs to mitigate threats caused by different malicious activities of intruders. The results show that our combination of data mining techniques provides better detection and classification of malicious flows than other solutions.
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