A novel anomaly detection system to assist network management in SDN environment

Carvalho, Luiz F.; Fernandes, Gilberto; Rodrigues, Joel J. P. C.; Mendes, Leonardo de Souza; Proença, Mário Lemes

doi:10.1109/icc.2017.7997214

Cited by 12 publications

(2 citation statements)

References 14 publications

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“…Attack events are determined in the study by assessing the variation in the network's logical topology for various attack kinds and measuring the distance requirements on a table. Using traffic analysis of packet flows, Carvalho et al [13] demonstrate a real-time anomaly detection and prevention system in SDN. The digital signature of flows acquired using the OF protocol is created by profiling normal traffic behavior.…”

Section: Related Workmentioning

confidence: 99%

Flow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approach

Zavrak

İskefiyeli

2023

Neural Comput & Applic

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In this study, the SAnDet architecture, which can do anomaly-based intrusion detection by taking advantage of the capabilities offered by the SDN architecture, is presented and implemented as a controller application. A detailed description of this system which consists of three main modules which are statistics collector, anomaly detector, and anomaly prevention is given. More specifically, Replicator Neural Networks (RNN) which is a special variant of the autoencoder, and the EncDecAD method which is a special type of LSTM network that can produce successful results, especially in given data series, are used to identify unknown attacks using flow features collected from OpenFlow switches. In experiments, flow-based features extracted from network traffic data including different types of attacks, are given as input into models as time series. The results of the methods are calculated using the ROC and AUC metrics. Experimental results show that EncDecAD outperforms RNN. Moreover, it is demonstrated that this study has several benefits over previously conducted research.

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Section: Related Workmentioning

confidence: 99%

Flow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approach

Zavrak

İskefiyeli

2023

Neural Comput & Applic

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“…Network measurement is the core of traffic engineering, such as load balance, 2 quick failure recovery, 3 and anomaly detection. 4 In SDN, flow is the basic unit of the data transmission, its transmission route is programmed by the centralized controller based on the source and destination of the flow. The forwarding rules in each switch on the route are programmed and installed by the controller remotely.…”

Section: Introductionmentioning

confidence: 99%

A software‐defined networks‐based measurement method of network traffic for 6G technologies

Huo

Jiang

2020

Trans Emerging Tel Tech

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In Software-Defined Networks (SDN) for 6G technologies, the controller should and efficiently measure the flow traffic in switches for traffic engineering. Fine-grained flow m can more accurately describe the traffic in the network, but it also consumes much more res reduce the overhead incurred in the measurement process and obtain the approximate f measurements, we propose a novel lightweight measurement scheme that runs in the controller lightweight measurement architecture consists of two parts: coarse-grained measurement and in optimization. In the first part, based on the SDN architecture, we use the pull-based random method to quickly obtain the coarse-grained measurement of flow traffic through OpenFlow prot second part, we insert some discrete values into the coarse-grained measurement with the in theory, then we optimize interpolation results until finding the optimal fine-grained f measurement by utilizing the multi-constraint method. We verified the feasibility of the measurement method, and simulation results show that the measurement error of the proposed under 25%, but the flow measurement overhead of this method occupies only 3.3% compared the fine-grained method.

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A survey and classification of the security anomaly detection mechanisms in software defined networks

et al. 2020

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A novel anomaly detection system to assist network management in SDN environment

Cited by 12 publications

References 14 publications

Flow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approach

Flow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approach

A software‐defined networks‐based measurement method of network traffic for 6G technologies

A survey and classification of the security anomaly detection mechanisms in software defined networks

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