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**11 - 17**of**17**### An Iterative Estimation of Data Window Size for Anomaly Detection using Self-Similar Feature in Network Traffic

"... Recently, network traffic is proven as a process that exhibits selfsimilar feature. It is believed that intrusive pattern of network traffic may change this statistical property and thus, it has been used in network anomaly detection research. However, there is no technique provides to identify appr ..."

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Recently, network traffic is proven as a process that exhibits selfsimilar feature. It is believed that intrusive pattern of network traffic may change this statistical property and thus, it has been used in network anomaly detection research. However, there is no technique provides to identify appropriate data window size in order to measure self-similarity feature. The measurements are not reliable if the data are insufficient. Otherwise, if the data are enormous, the intrusive data may hide inside the normal data and thus, the detections will fail. In this paper our intention is to observe the reliability of self-similarity measurement versus data window size. The observations have been done using optimization method and iterative estimation technique. The measurements are more reliable if the acceptance percentages of self-similarity model are increase. The result shown, these percentages are not linearly increase with the increment of window size. However, it can be divided into two different stages: first, drastic increment and secondly, it become unstable but moderately increase. Key words: Network anomaly detection, Self-similarity, Iterative estimation,

### ANALYSIS OF THE INFLUENCE OF SELF-SIMILAR TRAFFIC IN THE PERFORMANCE OF REAL TIME APPLICATIONS

"... In this work we present a network convergence environment and the results of the performance evaluation of real time applications in the presence of self similar traffic with different levels of burstiness. Our primary goal is to prove that the grooming of traffic flows that may result in different ..."

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In this work we present a network convergence environment and the results of the performance evaluation of real time applications in the presence of self similar traffic with different levels of burstiness. Our primary goal is to prove that the grooming of traffic flows that may result in different burstiness levels, represented by the Hurst parameter, has to be considered in the mapping of traffic flows in FEC (Forwarding Equivalence Class). We show the results of two different experiments and the latency, losses and interarrival time calculus related to a real time application. Also we make our considerations of how the traffic characterization procedure should promote an improvement in the development of traffic engineering procedures. KEY WORDS

### An Experimental Testbed for Evaluation Topics in Converged Networks

"... Abstract:-In this paper we present the development and validation of an experimental platform based in open source software and the development of a package for monitoring purposes. Also we show a group of tests executed on this platform and their results, which intend the validate the functioning a ..."

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Abstract:-In this paper we present the development and validation of an experimental platform based in open source software and the development of a package for monitoring purposes. Also we show a group of tests executed on this platform and their results, which intend the validate the functioning and support for future research which includes new ideas in the NGN (Next Generation Networks) topics, such as QoS (Quality of Service) mechanisms, traffic characterization and MPLS (Multi-protocol Label Switching) hybrid routing. We believe that the procedures shown in this paper, may give other research groups an overview of the primary steps for the implementation of a research lab with similar interests.

### unknown title

"... The intensity of Long-Range Dependence (LRD) for communications network traffic can be measured using the Hurst parameter. LRD characteristics in computer networks, however, present a fundamentally different set of problems in research towards the future of network design. There are various estimato ..."

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The intensity of Long-Range Dependence (LRD) for communications network traffic can be measured using the Hurst parameter. LRD characteristics in computer networks, however, present a fundamentally different set of problems in research towards the future of network design. There are various estimators of the Hurst parameter, which differ in the reliability of their results. Getting robust and reliable estimators can help to improve traffic characterization, performance modelling, planning and engineering of real networks. Earlier research [1] introduced an estimator called the Hurst Exponent from the Autocorrelation Function (HEAF) and it was shown why lag 2 in HEAF (i.e. HEAF (2)) is considered when estimating LRD of network traffic. This paper considers the robustness of HEAF(2) when estimating the Hurst parameter of data traffic (e.g. packet sequences) with outliers and also the reliability of HEAF(2).

### Long-range Dependent Self-similar Network Traffic: A Simulation Study to Compare Some New Estimators

"... Abstract- The intensity of long-range dependence (LRD) of the communications network traffic can be measured using the Hurst parameter. There are various estimators of Hurst parameter which differ in reliability of their results. Getting reliable estimator can help to improve traffic characterizatio ..."

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Abstract- The intensity of long-range dependence (LRD) of the communications network traffic can be measured using the Hurst parameter. There are various estimators of Hurst parameter which differ in reliability of their results. Getting reliable estimator can help to improve traffic characterization, performance modelling, planning and engineering of the real networks. This paper deals with the comparison of a few Hurst parameter estimators in standardized simulation experiment generating synthetic data sequences that exhibit long-range dependent features corresponding to observed data. The two best known classes of stationary processes with slowly decaying correlations (i.e., having long-range dependence) have been studied: fractional Gaussian noise (fGN) and fractional autoregressive integrated moving- average (FARIMA). Earlier authors introduced the estimator called “Hurst exponent from the autocorrelation function ” (HEAF) and in this work it is compared with an estimator based on the FARIMA process (FARIMA-H). Approximately unbiased versions are constructed and compared by simulation. I.

### 2005 3rd IEEE International Conference on Industrial Informatics (INDIN) Network traffic model for industrial environment

"... Abstract- In the paper a model of the traffic in the LAN is presented. In the model the most important components influ-encing the network traffic are taken into account. Namely, the transmission protocols and information buffering, operational systems and queuing algorithms as well as users ' ..."

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Abstract- In the paper a model of the traffic in the LAN is presented. In the model the most important components influ-encing the network traffic are taken into account. Namely, the transmission protocols and information buffering, operational systems and queuing algorithms as well as users ' behavior working with the network applications are considered. The model is based on an "on-off " function. The network traffic observed at the physical layer is a superposition of many se-quential and self-similar "on-off " processes. It has been shown that self-similarity of the traffic measured by Hurst parameter changing from almost 1.0 for very low frequencies to 0.5 for high frequencies. Implications, which are derived from the model, are described in the conclusion. Index Ternm-network protocols, traffic model, components traffic I.

### Hurst Parameter Estimation from Noisy Observations of Data Trafc Traces

"... Abstract: Data trafc traces are known to be bursty with long range dependence. The exact self-similarity model of long range dependence can pose analytical and practical problems at very small and very large time lags. In our model, the time series of the trafc trace (referred to as the signal) is a ..."

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Abstract: Data trafc traces are known to be bursty with long range dependence. The exact self-similarity model of long range dependence can pose analytical and practical problems at very small and very large time lags. In our model, the time series of the trafc trace (referred to as the signal) is assumed to possess an autocovariance prole corresponding to exact self-similarity over a range of lags, fkg, satisfying M < k < L. At lower lags, exact self-similarity may breakdown, or additive moving average type noise (inaccuracies) may corrupt the autocovariances. At very high lags, far beyond the number of observed samples, the autocovariance structure is irrelevant and may be assumed to be innite summable. Therefore, L can be as large as desired. Applications of such a model are discussed. The mean, variance, and the Hurst parameter of the signal, as well as the autocovariances of any independent zero mean moving average type additive noise are assumed to be unknown. A class of linear combinations of sample average second order statistics of noisy observations is constructed. They are unbiased estimates of their corresponding expectations. These expectations are shown to be devoid of the noise statistics. The ratio of two such expectations eliminates the signal variance. The ratio is a well behaved monotonic function of the only remaining unknown, the Hurst parameter. Equating the ratio of these expectations to the ratio of the corresponding sample averages from the noisy observations leads to a very easily solvable nonlinear equation with a unique root. The result and related issues are discussed.