OpenFlowSIA: An optimized protection scheme for software-defined networks from flooding attacks

Phan, Trung V.; Toan, Truong Van; Tuyen, Dang Van; Hương, Trương Thu; Thanh, Nguyen Huu

doi:10.1109/cce.2016.7562606

Cited by 44 publications

(30 citation statements)

References 7 publications

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“…Traffic analysis and policy creation at the Analyzer performance status, otherwise (F(x)= -1) the sample x i is assigned to the data group representing a performance degradation of the switch. Interested readers are referred to[11],[13]-[15] for a detailed explanation of the SVM algorithm.Thereasons for choosing the tuple ( f ,∆ f ) for evaluating the forwarding performance of a SDN switch are as follows: The effort for flow searching and matching within a SDN switch is proportional to the number and matching fields of flow entries. Moreover, a SDN switch has a maximum capacity ( f cap ) for storing the flow entries.…”

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confidence: 99%

Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks

Hajizadeh

Khai

Bauschert

2019

2019 International Conference on Networked Systems (NetSys)

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In this paper, we propose a destination-aware adaptive traffic flow rule aggregation (DATA) mechanism for facilitating traffic flow monitoring in SDN-based networks. This method adapts the number of flow table entries in SDN switches according to the level of detail of traffic flow information that other mechanisms (e.g. for traffic engineering, traffic monitoring, intrusion detection) require. It also prevents performance degradation of the SDN switches by keeping the number of flow table entries well below a critical level. This level is not preset as a hard threshold but learned during operation by using a machinelearning based algorithm. The DATA method is implemented within a RESTful application (DATA App) which monitors and analyzes the ongoing network traffic and provides instructions to the SDN controller to adapt the traffic flow matching strategies accordingly. A thorough performance evaluation of DATA is conducted in an SDN emulation environment. The results show that-compared to the default behavior of common SDN controllers-the proposed DATA approach yields significant SDN switch performance improvements while still providing detailed traffic flow information on demand.

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confidence: 99%

Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks

Hajizadeh

Khai

Bauschert

2019

2019 International Conference on Networked Systems (NetSys)

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“…In [13] and [14], SVM is used to detect DDoS attacks quite efficiently. K-Nearest Neighbor and graph theory are combined to classify DDoS attacks from benign flows in SDNs by AlEroud et al in [15].…”

Section: Related Workmentioning

confidence: 99%

Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Tang

Mhamdi

McLernon

et al. 2018

2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)

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“…SOM-a neural network-based technique-is used as the classification method. SVM was used in [17,18] to detect DDoS attacks in SDNs quite efficiently. One-class SVM was trained with a malicious dataset for a low false alarm rate in [19].…”

Section: Related Workmentioning

confidence: 99%

DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

et al. 2020

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Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach.

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OpenFlowSIA: An optimized protection scheme for software-defined networks from flooding attacks

Cited by 44 publications

References 7 publications

Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks

Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks

Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

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