Revisiting Traffic Anomaly Detection Using Software Defined Networking

By Syed Akbar Mehdi, Junaid Khalid, Syed Ali Khayam. International Symposium on Recent Advances in Intrusion Detection, 2011

391 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/a47972b7f88d59d2a3250a64cafa6a9c984603b0

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Citations in the corpus

A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks (2014, methodology): “[72] argues that an Anomaly Detection System (ADS) implemented within a programmable home network provides a more accurate identification of malicious activity as compared to one deployed at the ISP; additionally, the implementation would be able to operate at line rate with no performance penalty, ”
A Survey and a Layered Taxonomy of Software-Defined Networking (2014, background): “[94] propose to bring ADS from network core”
Software-defined networking (SDN): a survey (2016, methodology): “[146] Anomaly detection algorithms that use OpenFlow flow information for traffic anomaly detection including the detection of scanning worms Exploiting programmability of SDN to provide accurate anomaly detection capabilities in home networking IN/EX ST UST AC/ PS CT/ SB/ AP”
Survey on SDN based network intrusion detection system using machine learning approaches (2018, methodology): “Machine learning techniques (ML) is applied to develop NIDS to improve detection accuracy [5] and low false alarm rate [4].”
Deep learning approach for Network Intrusion Detection in Software Defined Networking (2016, methodology): “By using the programability of SDN, the authors of [13] show that a programmable home network router can provide the ideal platform and location in the network for detecting security problems in a SOHO (Small Office/Home Office) network.”
A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation (2014, background): “In [108] it proposes 4 different OpenFlow traffic anomaly detection algorithms.”
A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN) (2016, methodology): “Finally, all layers of SAE are treated as a single model and fine-tuned to improve the performance of the model shown in Figure 2c.”
A Survey on Emerging SDN and NFV Security Mechanisms for IoT Systems (2019, background): “The advantages to perform anomaly detection mechanisms at the network edge have been experimentally proved in [124], where implemented detection algorithms,”
Security in Software Defined Networks: A Survey (2015, positive): “The use of SDN for improving anomaly detection systems in small and home networks is demonstrated in [87].”
Sdn Security: A Survey (2013, methodology): “[17], [24] X X X X X”
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 filter removes uninteresting traffic based on the premise that the first few packets of a connection request are sufficient for traffic anomaly detection [73].”
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.”
AVANT-GUARD: scalable and vigilant switch flow management in software-defined networks (2013, methodology): “developed SDN applications for detecting network scan attacks [17].”
Software Defined Networking Architecture, Security and Energy Efficiency: A Survey (2017, background): “, SDN IPS [12], TIPS [13], anomaly attacks [14]), energy efficiency and network virtualization for enhancing the overall network performance.”
FRESCO: Modular Composable Security Services for Software-Defined Networks (2013, methodology): “For example, our FRESCO implementations demonstrate over 90% reduction in lines of code when compared to standard implementations and recently published OpenFlow implementations [24].”
DDoS Attack Detection and Mitigation Using SDN: Methods, Practices, and Solutions (2017, methodology): “Experiments have been conducted using various controllers which include using NOX controller [42], BeaconController [56], andFloodlight controller [63].”