Large Deviation Multifractal Analysis of a Class of Additive Processes With Correlated Nonstationary Increments

Véhel, Jacques Lévy; Rams, Michał

doi:10.1109/tnet.2012.2229469

Cited by 5 publications

(1 citation statement)

References 25 publications

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“…If τ(q) is nonlinear in q, X(t) is called multifractal; otherwise, it is called monofractal. The multifractal of network traffic results from the congestion control mechanism of TCP and the convergence of multiple TCP connections 7,8 According to statistics, more than 95% of the total number of bytes and more than 90% of the total number of packets are transmitted by TCP in Internet, 27 so the network traffic present multifractal in general. A network model that consists of N TCP connections is shown as Figure 1.…”

Section: Multifractal Model Of Network Trafficmentioning

confidence: 99%

Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network

Liu

Wang

2017

Int J Communication

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Summary As a special type of denial of service (DoS) attacks, the TCP‐targeted low‐rate denial of service (LDoS) attacks have the characteristics of low average rate and strong concealment, so it is difficult to identify such attack traffic. As multifractal characteristics exist in network traffic, a new identification approach based on wavelet transform and combined neural network is proposed to classify normal network traffic and LDoS attack traffic. Wavelet energy spectrum coefficients extracted from the sampled traffic are used for multifractal analysis of traffic over different time scale. The combined neural network is designed to classify these multiscale spectrum coefficients that show different multifractal characteristics belonging to normal network traffic and LDoS attack traffic. Test results of test‐bed experiments indicate that the proposed approach can identify LDoS attack traffic accurately.

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Section: Multifractal Model Of Network Trafficmentioning

confidence: 99%

Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network

Liu

Wang

2017

Int J Communication

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Analysis of large-deviation multifractal spectral properties through successive compression for double JPEG detection

Gavrovska

2023

Multimed Tools Appl

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Modern network traffic modeling based on binomial multiplicative cascades

Stênico

Ling

2014

J Supercomput

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In this paper we present a new multifractal approach for modern network traffic modeling. The proposed method is based on a novel construction scheme of conservative multiplicative cascades. We show that the proposed model can faithfully capture some main characteristics (scaling function and moment factor) of multifractal processes. For this new network traffic model, we also explicitly derive analytical expressions for the mean and variance of the corresponding network traffic process and show that its autocorrelation function exhibits long-range dependent characteristics. Finally, we evaluate the performance of our model by testing both real wired and wireless traffic traces, comparing the obtained results with those provided by other well-known traffic models reported in the literature. We found that the proposed model is simple and capable of accurately representing network traffic traces with multifractal characteristics.

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Large Deviation Multifractal Analysis of a Class of Additive Processes With Correlated Nonstationary Increments

Cited by 5 publications

References 25 publications

Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network

Identifying LDoS attack traffic based on wavelet energy spectrum and combined neural network

Analysis of large-deviation multifractal spectral properties through successive compression for double JPEG detection

Modern network traffic modeling based on binomial multiplicative cascades

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