Robust estimation of the self-similarity parameter in network traffic using wavelet transform

2007 ◽  
Vol 87 (9) ◽  
pp. 2111-2124 ◽  
Author(s):  
Haipeng Shen ◽  
Zhengyuan Zhu ◽  
Thomas C.M. Lee
Keyword(s):  
Wavelet Transform ◽  
Robust Estimation ◽  
Network Traffic ◽  
The Self ◽  
Self Similarity ◽  
Similarity Parameter

scholarly journals Statistical research and modeling network traffic

2021 ◽  
Vol 244 ◽  
pp. 07002
Author(s):  
Tatiana Tatarnikova ◽  
Igor Sikarev ◽  
Vladimir Karetnikov ◽  
Artem Butsanets
Keyword(s):  
Network Traffic ◽  
Least Squares Method ◽  
Regression Line ◽  
The Self ◽  
Logarithmic Scale ◽  
Self Similarity ◽  
Statistical Research ◽  
Similarity Parameter ◽  
Real Network ◽  
Self Similar

The self-similarity properties of the considered traffic were checked on different time scales obtained on the available daily traffic data. An estimate of the tail severity of the distribution self-similar traffic was obtained by constructing a regression line for the additional distribution function on a logarithmic scale. The self-similarity parameter value, determined by the severity of the distribution “tail”, made it possible to confirm the assumption of traffic self-similarity. A review of models simulating real network traffic with a self-similar structure was made. Implemented tools for generating artificial traffic in accordance with the considered models. Made comparison of artificial network traffic generators according to the least squares method criterion for approximating the artificial traffic point values by the approximation function of traffic. Qualitative assessments traffic generators in the form of the software implementation complexity were taken into account, which, however, can be a subjective assessment. Comparative characteristics allow you to choose some generators that most faithfully simulate real network traffic. The proposed sequence of methods to study the network traffic properties is necessary to understand its nature and to develop appropriate models that simulate real network traffic.

Estimation of the self-similarity parameter using the wavelet transform

2004 ◽  
Vol 84 (1) ◽  
pp. 117-123 ◽  
Author(s):  
S. Soltani ◽  
P. Simard ◽  
D. Boichu
Keyword(s):  
Wavelet Transform ◽  
The Self ◽  
Self Similarity ◽  
Similarity Parameter

On the self-similarity of traffic generated by network traffic simulators

2015 ◽  
pp. 285-311 ◽  
Author(s):  
Diogo A.B. Fernandes ◽  
Miguel Neto ◽  
Liliana F.B. Soares ◽  
Mário M. Freire ◽  
Pedro R.M. Inácio
Keyword(s):  
Network Traffic ◽  
The Self ◽  
Self Similarity

scholarly journals Tinklo apkrovos savastingumo tyrimas realiu laiku

2010 ◽  
Vol 53 ◽  
pp. 100-105
Author(s):  
Liudvikas Kaklauskas ◽  
Leonidas Sakalauskas
Keyword(s):  
Network Traffic ◽  
Stability Index ◽  
The Self ◽  
Hurst Index ◽  
Robust Method ◽  
Self Similarity ◽  
Software Module ◽  
On Line ◽  
Computer Based ◽  
Increment Ratio

Straipsnyje analizuojami indikatoriai, taikomi tinklo apkrovos savastingumui tirti: Hursto indeksas, stabilumo indeksas, IR (Increment Ratio) statistika. Kompiuteriniu modeliavimu ištirtas šių indikatorių tinkamumas tinklo apkrovos savastingumui vertinti realiu laiku. Sukurta programinių modulių biblioteka SSE (Self-Similarity Estimator), skirta fiksuoti ir agreguoti tinklo duomenų paketus, vertinanti tinklo apkrovos srautų savastingumą realiu laiku. Naudojant SSE programinių modulių biblioteką, suformuotų laiko eilučių Hursto indeksas ir IR statistika apskaičiuotos naudojant analitines formules, o stabilumo indeksas – robastiniu empirinių kvantilių regresijos metodu. Modulių bibliotekos SSE analizės efektyvumas ištirtas kompiuterinio modeliavimo būdu apskaičiuojant savastingumo indikatorius stabiliųjų procesų realizacijoms.Pagrindiniai žodžiai: savastingumas (self-similarity), Hursto indeksas, stabilumo indeksas, IR statistika.The Real-time Mode Research of Network Traffic FractalityLiudvikas Kaklauskas, Leonidas Sakalauskas Summaryhe article analyses the indicators implemented for investigating the network self-similarity: the Hurst index, stability index, IR (Increment Ratio) statistics. The suitability of these indicators for the on-line estimation of network traffic self-similarity was investigated by applying computer-based modelling. The software SSE (Self-Similarity Estimator) module library was developed; it was designed for the recording and aggregation of network traffic packages as well as for the on-line estimation of network traffic self-similarity. By using the SSE software module library, the Hurst index and the IR statistics of time series were estimated by applying analytic formulas, and the index of stability was estimated applying the robust method of regression of empirical quantiles. The efficiency of the analysis of the SSE module library was investigated by estimating the self-similarity indicators for realisation of the stabile processes while applying the method of computer-based modelling.

scholarly journals Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter

2019 ◽  
Vol 11 (4) ◽  
pp. 451 ◽  
Author(s):  
Shengwu Tong ◽  
Xiuguo Liu ◽  
Qihao Chen ◽  
Zhengjia Zhang ◽  
Guangqi Xie
Keyword(s):  
Wind Speed ◽  
Random Forest ◽  
Oil Spill ◽  
The Self ◽  
Detection Accuracy ◽  
Self Similarity ◽  
Polarimetric Sar ◽  
Similarity Parameter ◽  
Oil Spill Detection ◽  
Sar Data

Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, a novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy in this paper. In this method, the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes. The proposed method uses the Random Forest classification combing self-similarity parameter with seven well-known features to improve oil spill detection accuracy. Evaluations and comparisons were conducted with Radarsat-2 and UAVSAR polarimetric SAR datasets, which shows that: (1) the oil spill detection accuracy of the proposed method reaches 92.99% and 82.25% in two datasets, respectively, which is higher than three well-known methods. (2) Compared with other seven polarimetric features, self-similarity parameter has the better oil spill detection capability in the scene with lower wind speed close to 2–3 m/s, while, when the wind speed is close to 9–12 m/s, it is more suitable for oil spill detection in the downwind scene where the microwave incident direction is similar to the sea surface wind direction and performs well in the scene with incidence angle range from 29.7° to 43.5°.

Analysis of the Impact of Intensive Attacks on the Self-Similarity Degree of the Network Traffic

2008 ◽  
Author(s):  
Pedro R. M. Inácio ◽  
Mário M. Freire ◽  
Manuela Pereira ◽  
Paulo P. Monteiro
Keyword(s):  
Network Traffic ◽  
The Self ◽  
Self Similarity ◽  
Similarity Degree ◽  
The Impact

scholarly journals Remarks on Confidence Intervals for Self-Similarity Parameter of a Subfractional Brownian Motion

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Junfeng Liu ◽  
Litan Yan ◽  
Zhihang Peng ◽  
Deqing Wang
Keyword(s):  
Brownian Motion ◽  
Confidence Intervals ◽  
The Self ◽  
Concentration Inequalities ◽  
Self Similarity ◽  
Similarity Parameter ◽  
One Dimensional ◽  
Convergence Results ◽  
Subfractional Brownian Motion ◽  
Quadratic Variations

We first present two convergence results about the second-order quadratic variations of the subfractional Brownian motion: the first is a deterministic asymptotic expansion; the second is a central limit theorem. Next we combine these results and concentration inequalities to build confidence intervals for the self-similarity parameter associated with one-dimensional subfractional Brownian motion.

Effects of upper‐layer protocols on the self‐similarity of network traffic

2001 ◽  
Vol 84 (7) ◽  
pp. 19-30 ◽  
Author(s):  
Yoshiaki Sumida ◽  
Hiroyuki Ohsaki ◽  
Masayuki Murata ◽  
Hideo Miyahara
Keyword(s):  
Network Traffic ◽  
The Self ◽  
Self Similarity
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