Modeling network traffic for traffic matrix estimation and anomaly detection based on Bayesian network in cloud computing networks

Nie, Laisen; Jiang, Dingde; Lv, Zhihan

doi:10.1007/s12243-016-0546-3

Cited by 44 publications

(29 citation statements)

References 20 publications

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“…In past several studies (see [7], [8], [9] and [10]), researchers have employed classical machine learning mechanism such as SVM, K-Nearest Neighbour (KNN), ANN, Random Forest etc. for developing an IDS.…”

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)

241

129

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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)

241

129

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“…Before the development of DNN variants, classical ML algorithms, such as random forest (RF), SVM, ANN, and k-nearest neighbors (KNN) were used by various researchers to develop IDSs [9][10][11][12]. However, these methods have inherent limitations.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments

Albahar

2019

Security and Communication Networks

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Software-defined networking (SDN) is a promising approach to networking that provides an abstraction layer for the physical network. This technology has the potential to decrease the networking costs and complexity within huge data centers. Although SDN offers flexibility, it has design flaws with regard to network security. To support the ongoing use of SDN, these flaws must be fixed using an integrated approach to improve overall network security. Therefore, in this paper, we propose a recurrent neural network (RNN) model based on a new regularization technique (RNN-SDR). This technique supports intrusion detection within SDNs. The purpose of regularization is to generalize the machine learning model enough for it to be performed optimally. Experiments on the KDD Cup 1999, NSL-KDD, and UNSW-NB15 datasets achieved accuracies of 99.5%, 97.39%, and 99.9%, respectively. The proposed RNN-SDR employs a minimum number of features when compared with other models. In addition, the experiments also validated that the RNN-SDR model does not significantly affect network performance in comparison with other options. Based on the analysis of the results of our experiments, we conclude that the RNN-SDR model is a promising approach for intrusion detection in SDN environments.

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“…Such schemes may prove beneficial if the routing matrix (A) exhibits a highly decaying Eigen spectrum [11]. Other notable contribution in the field of traffic matrix estimation include, Bayesian Learning techniques for traffic matrix estimation, investigated by Nie [23] and Xiaobo [24]. Other researchers [25][26][27][28] have focused their attention on the optimal placement of network monitors for scalable monitoring of the network.…”

Section: Related Workmentioning

confidence: 99%

“…Qazi and Moors have studied the issue of estimation of network parameters through network tomography when the underlying network model is estimated with errors [31] and Qazi et al studied the applications of genetic algorithms to the network tomography problem [32]. The problem of the presence of heterogeneous traffic due to the cloud based applications has further increased the complexity of network tomography based estimation techniques [23]. Probability Model based Traffic Matrix Estimation technique has been covered for star networks and other general topologies by Tian et al [33]; However they rely on several assumptions to simplify the model such as fixed routing probability between nodes and do not address, the problem of the implementation of this technique for more general Internet based software defined cloud networks.…”

Section: Related Workmentioning

confidence: 99%

A Novel Compressed Sensing Technique for Traffic Matrix Estimation of Software Defined Cloud Networks

Qazi¹,

Atif²,

Kadri³

2018

KSII TIIS

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Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under-constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

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Modeling network traffic for traffic matrix estimation and anomaly detection based on Bayesian network in cloud computing networks

Cited by 44 publications

References 20 publications

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

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

Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments

A Novel Compressed Sensing Technique for Traffic Matrix Estimation of Software Defined Cloud Networks

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