Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments

By Kostas Giotis, C. Argyropoulos, G. Androulidakis, D. Kalogeras, B. Maglaris. Comput. Networks, 2014

452 citations recorded by the source corpus.

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DDoS Attack Detection and Mitigation Using SDN: Methods, Practices, and Solutions (2017, methodology): “gies that are extensively deployed in SDN [34,58,59,66,71, 72].”
Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges (2016, methodology): “In order to improve the scalability of proposed native OF approaches in [75], a new method combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments is presented by [78].”
A Survey and a Layered Taxonomy of Software-Defined Networking (2014, background): “been proposing initial solutions for such a problem [93], [97], [98], however, major efforts need to be spent in this area in order to propose solutions with good trade-offs between performance, usability and security.”
Deep learning approach for Network Intrusion Detection in Software Defined Networking (2016, methodology): “In order to improve the scalability of native OpenFlow, a new method combining OpenFlow and sFlow has been proposed in [16] for an effective and scalable anomaly detection and mitigation mechanism in an SDN environment.”
A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN) (2016, background): “In [15], Giotis et al. combined an OpenFlow (OF) and sFlow for anomaly detection to reduce processing overhead in native OF statistics collection.”