Visible to the public SDN/NFV-Based DDoS Mitigation via Pushback

TitleSDN/NFV-Based DDoS Mitigation via Pushback
Publication TypeConference Paper
Year of Publication2020
AuthorsBülbül, Nuref\c san Sertba\c s, Fischer, Mathias
Conference NameICC 2020 - 2020 IEEE International Conference on Communications (ICC)
Date PublishedJune 2020
PublisherIEEE
ISBN Number978-1-7281-5089-5
KeywordsAggregates, cloud computing, Collaboration, composability, Computer crime, DDoS, DDoS attack mitigation, DDOS attacks detection, Human Behavior, IP networks, Metrics, NFV, pattern generation, pubcrawl, pushback, resilience, Resiliency, SDN, Servers
AbstractDistributed Denial of Service (DDoS) attacks aim at bringing down or decreasing the availability of services for their legitimate users, by exhausting network or server resources. It is difficult to differentiate attack traffic from legitimate traffic as the attack can come from distributed nodes that additionally might spoof their IP addresses. Traditional DoS mitigation solutions fail to defend all kinds of DoS attacks and huge DoS attacks might exceed the processing capacity of routers and firewalls easily. The advent of Software-defined Networking (SDN) and Network Function Virtualization (NFV) has brought a new perspective for network defense. Key features of such technologies like global network view and flexibly positionable security functionality can be used for mitigating DDoS attacks. In this paper, we propose a collaborative DDoS attack mitigation scheme that uses SDN and NFV. We adopt a machine learning algorithm from related work to derive accurate patterns describing DDoS attacks. Our experimental results indicate that our framework is able to differentiate attack and legitimate traffic with high accuracy and in near-realtime. Furthermore, the derived patterns can be used to create OpenFlow (OF) or Firewall rules that can be pushed back into the direction of the attack origin for more efficient and distributed filtering.
URLhttps://ieeexplore.ieee.org/document/9148717
DOI10.1109/ICC40277.2020.9148717
Citation Keybulbul_sdnnfv-based_2020