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The investigation and forecasting network traffic usage is an essential concern in the academic activities of university. This paper reports how to apply and compare SARIMA, NARX, and BPNN by using short-term time series datasets. The network traffic datasets are obtained from the ICT Universitas Mulawarman. As a result, the determination of several prediction models will continue to be an alternative...
The work presents results of a numerical study of fractal characteristics of multifractal stream at addition of stream, which does not have multifractal properties. They showed that the generalized Hurst exponent of total stream tends to one of original multifractal stream with increase in signal/noise ratio.
The goal of this paper is to prove the potential of fractal analysis techniques in evaluation of network characteristics, especially in detection of anomalies, as a method to reveal self-similarities in generated traffic. After a short review of some anomaly detection methods, one describe in detail a statistical signal processing technique based on abrupt change detection. A case study based on real...
Sharing of information leads to the need to transfer data between geographically distant locations. Identifying the most appropriate time period to execute the data transfer is essential to achieve the best data transfer throughput; e.g. one can forecast network traffic, identify future low network traffic activities between two entities, and plan the data transfer accordingly. This forecasting can...
Learning and identifying events in network traffic is crucial for service providers to improve their mobility network performance. In fact, large special events attract cell phone users to relative small areas, which causes sudden surge in network traffic. To handle such increased load, it is necessary to measure the increased network traffic and quantify the impact of the events, so that relevant...
The real data of the network traffic were analyzed to find out characteristic parameters for autonomic provisioning. An observed strong non-linearity (bursts) leads to heteroskedastic (time dependent conditional variance) model applications. The non-linear time series model and statistical method were applied. It was found that upper limit of burst variations could be quantitatively estimated with...
Time series can be decomposed into different spectrum sub-sequence using wavelet decomposition, and restoring all the time series prediction from the decomposition can effectively improve the prediction accuracy. This paper presents a method of network traffic prediction named AWARIMA. First we choose a 5-layer db3 wavelet to decompose network traffic data,make the similar sub-sequences from the wavelet...
In this paper we evaluate an online load change detection algorithm, aimed to identify changes in traffic loads when monitoring Internet links. This online change detector was first introduced in [1] and produces an alert when a sustained and statistically significant change has been detected. Then, the network manager verifies the change and takes action if the change is truly relevant. We show that...
Several methods used for measure network traffic are introduced in this paper. Hurst parameter and Characteristic of R/S algorithm are analyzed in details. An online real-time Hurst parameter measure algorithm based on R/S algorithm is proposed, and algorithm steps are demonstrated by using emulation experiment. The experiment result shows that the online real -time Hurst parameter detection algorithm...
Campus networkpsilas Internet accessing traffic is complicated, non-linear and periodical. Our goal is to give out a engineering approach to prediction of network traffic based time-series analysis model (EPTS) for campus exit-link. In our EPTS with rate-limiting, we configure rate limit based interface, then use time-series decomposed model, give out the linear trend component, periodical component,...
Real-time media streaming in wireless network has seen increased demand on the Internet in recent years, and has drawn tremendous attention from both academia and industry as well. Preliminary traffic measurements demonstrate that data traffic in wireless communication also exhibits self-similarity. However, little research on wireless media streaming is seen in publication. The purpose of this paper...
This work aimed to show that time series are an excellent tool for data traffic modelling within Wi-Fi networks. Box-Jenkins methodology, which is herein described, was used to achieve this objective. Wi-Fi traffic modelling through correlated models, like time series, allow to adjust a great part of the data behavior dynamics in a single equation and, based on it, to estimate traffic future values...
The main drawback of traditional intrusion detection systems makes anomaly detection systems an active research area. In this paper we introduce a novel network-based anomaly detection approach using stochastic learning automata. The paper main objective is to construct a network-based statistical anomaly detection system capable of classifying the ensemble network broadcast traffic as normal or abnormal...
Performance of data communication networks is influenced by many factors, e.g. routing algorithms, traffic load, network connection topology. Using functional fixed effect models we study how the factors, routing cost metric, source load with various levels and their interactions affect a response metric, a network performance indicator ldquonumber of packets in transitrdquo. Our focus is on the study...
Traffic models play an important role in network design and performance evaluation. Since the usual assumption of independencies between different traffic streams, these models are not able to precisely reflect the characteristic of real network traffics. In this paper, we present a joint distribution traffic model which can express the dependency of two traffics. This model is constructed with parameters...
The paper makes models the Internet traffic demand by applying statistical techniques on data collected between two nodes of Pakistan's Internet backbone over a period of two years. The traffic model is used to make predictions for future Internet usage which simplifies the task of capacity planning for the network management by helping them determine when and to what extent future provisioning is...
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