Lightweight DDoS flooding attack detection using NOX/OpenFlow

By Rodrigo Braga, Edjard de Souza Mota, Alexandre Passito. IEEE Local Computer Network Conference, 2010

734 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/deeec803eaceec8cd5254c64e73b67959c5e7670

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Citations in the corpus

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, methodology): “In [210], a lightweight DDoS attack detection method is proposed and implemented on a NOX-based SDN network.”
A Survey and a Layered Taxonomy of Software-Defined Networking (2014, background): “[95] propose a security application to detect Distributed Denial of Service (DDoS) attacks for NOX controllers.”
Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments (2014, methodology): “The first one is based on an already proposed native OF approach [13,14], in which periodical queries and replies from flow-entries of the switch are collected.”
Network Innovation using OpenFlow: A Survey (2014, methodology): “[43] DDoS attack detection Statistic information in the flow table is used to classify traffic as normal or malicious.”
Deep learning approach for Network Intrusion Detection in Software Defined Networking (2016, methodology): “A lightweight method for DDoS attack detection based on traffic flow features is presented in [15] with extraction of 6tuple features: Average of Packets per flow (APf), Average of Bytes per flow (ABf), Average of Duration per flow (ADf), Percentage of Pair-flows (PPf), Growth of Single-flows (GSf),”
Security in Software Defined Networks: A Survey (2015, positive): “A lightweight DDoS ﬂooding attack detection using Self-Organizing Maps (SOM) [78] is presented in [64].”
Security for 5G and Beyond (2019, methodology): “For instance, self-organizing maps [209] are used in [210] against lightweight DoS and Distributed DoS (DDoS)attacks.”
Sdn Security: A Survey (2013, methodology): “In [17], the authors present a Distributed DoS (DDoS) detection method based on several traf ﬁ c ﬂ ow features.”
Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges (2016, methodology): “The method is divided into three modules [75]:”
A Survey on Emerging SDN and NFV Security Mechanisms for IoT Systems (2019, background): “For example, several SDN-based strategies have been implemented to timely detect DDoS attacks [119], [120].”
A Survey of Security in Software Defined Networks (2016, methodology): “In [104], a Distributed DoS (DDoS) detection method is presented.”
A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN) (2016, methodology): “[20] used 3s for polling interval, we found it too frequent for polling and invoking the FE.”
InSDN: A Novel SDN Intrusion Dataset (2020, methodology): “Braga et al. (2010) simulated the SDN network to test ML models against DDoS ﬂooding attacks [32].”
Software Defined Networking Architecture, Security and Energy Efficiency: A Survey (2017, positive): “Lightweight DDoS attack detection [119] has been proposed”
DDoS Attack Detection and Mitigation Using SDN: Methods, Practices, and Solutions (2017, methodology): “Self-organizing map, one of the most popular neural network models, is used by Braga et al. [55] This work shows successful DDoS attack detection with high rate of detection and very low rate of false alarms.”