Study of the Impact of Self-Similarity on the Network Node Traffic

1970 ◽  
Vol 111 (5) ◽  
pp. 27-32
Author(s):  
L. Kaklauskas ◽  
L. Sakalauskas
Keyword(s):  
Network Traffic ◽  
Buffer Capacity ◽  
Service Discipline ◽  
Network Node ◽  
Self Similarity ◽  
Packet Losses ◽  
Queue Discipline ◽  
Self Similar ◽  
Stochastically Bounded ◽  
The Impact

The article analyses a stochastically bounded the GI/G/m//N circuit switched network model with packet losses, with stochastic input network traffic, stochastic served network node, and deterministic and finite network node buffer capacity. Max-plus algebra instrumentality is used for the network processes analysis. FIFO tail drop or LIFO tail drop buffer is used. We have established that the average waiting time in the queue had increased when the queue service discipline was FIFO as compared with LIFO, while the offered traffic was Poisson and the served in the node traffic was self-similar. The network traffic is served faster in the network node with the buffer queue discipline LIFO, while the offered traffic is Poisson and its intensity exceeds the served in the node traffic 10 times. Ill. 2, bibl. 24 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.350

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 Optimising Self-Similarity Network Traffic for Better Performance

2020 ◽  
pp. 164-176
Author(s):  
Ikharo A. B. ◽  
Anyachebelu K. T. ◽  
Blamah N. V. ◽  
Abanihi V. K.
Keyword(s):  
Network Traffic ◽  
Network Performance ◽  
Computer Network ◽  
Ann Model ◽  
Performance Measurements ◽  
Self Similarity ◽  
Traffic Modelling ◽  
Backpropagation Network ◽  
Self Similar

Given the ubiquity of the burstiness present across many networking facilities and services, predicting and managing self-similar traffic has become a key issue owing to new complexities associated with self-similarity which makes difficult the achievement of high network performance and quality of service (QoS). In this study ANN model was used to model and simulate FCE Okene computer network traffic. The ANN is a 2-39-1 Feed Forward Backpropagation network implemented to predict the bursty nature of network traffic. Wireshark tools that measure and capture packets of network traffic was deployed. Moreover, variance-time method is a log-log scale plot, representing variance versus a non-overlapping block of size m aggregate variance level engaged to established conformity of the ANN approach to self-similarity characteristic of the network traffic. The predicted series were then compared with the corresponding real traffic series. Suitable performance measurements used were the Means Square Error (MSE) and the Regression Coefficient. Our results showed that burstiness is present in the network across many time scales. The study also established the characteristic property of a long-range dependence (LRD). The work recommended that network traffic observation should be longer thereby enabling larger volume of traffic to be capture for better accuracy of traffic modelling and prediction.

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.

scholarly journals On network traffic statistical analysis

2020 ◽  
Vol 48 ◽  
Author(s):  
Liudas Kaklauskas ◽  
Leonidas Sakalauslas
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Statistical Method ◽  
Network Traffic ◽  
Network Node ◽  
Hurst Index ◽  
Self Similarity ◽  
Present Measurement ◽  
Traffic Characteristics ◽  
Measurement Results

The present article deals with statistical university network traffic, by applying the methods of self-similarity and chaos analysis. The object of measurement is Šiauliai University LitNet network node maintaining institutions of education of the northern Lithuania region. Time series of network traffic characteristics are formed by registering amount of information packets in a node at different regimes of network traffic and different values of discretion of registered information are present. Measurement results are processed by calculating Hurst index and estimating reliability of analysis results by applying the statistical method. Investigation of the network traffic allowed us drawing conclusions that time series bear features of self-similarity when aggregated time series bear features of slowly decreasing dependence.

Research on Self-Similarity Network Traffic Modeling

2011 ◽  
Vol 110-116 ◽  
pp. 2859-2865
Author(s):  
Yu Zhang ◽  
Teng Fei Yin
Keyword(s):  
Network Traffic ◽  
Network Performance ◽  
Mapping Function ◽  
Random Variable ◽  
Self Similarity ◽  
Mapping Model ◽  
Chaotic Mapping ◽  
Self Similar ◽  
Heavy Tailed ◽  
Definition Of

This paper introduces the phenomenon of self-similar network, and then it gives the mathematical definition of self-similar and analysis for the network performance. Based on this, this paper puts forward a new mapping model of ON / OFF and the chaotic mapping model based on the ideas. The model simplifies the chaotic mapping function mapping model by choosing a random variable with a linear piecewise function. The model length is subject to the state heavy-tailed. This model can capture network traffic self-similarity.

ANALYSIS OF THE PROCESS OF SELFSIMILARITY OF NETWORK TRAFFIC AS AN APPROACH TO DETECTING CYBER ATTACKS ON COMPUTER NETWORKS

2020 ◽  
Author(s):  
И.В. КОТЕНКО ◽  
А.М. КРИБЕЛЬ ◽  
О.С. ЛАУТА ◽  
И.Б. САЕНКО
Keyword(s):  
Brownian Motion ◽  
Computer Networks ◽  
Network Traffic ◽  
Telecommunication Network ◽  
Cyber Attacks ◽  
Self Similarity ◽  
Data Set ◽  
Fractal Brownian Motion ◽  
Self Similar

Предложен подход кобнаружению кибератак на компьютерные сети, основанный на выявлениианомалий в сетевом трафике путем оценки свойства самоподобия. Рассмотрены методы выявления долговременной зависимости в фрактальном броуновском движении и реальном сетевом трафике компьютерных сетей. Показано, что трафик телекоммуникационной сети является самоподобной структурой и его поведение близко к фрактальному броуновскому движению. В качестве инструментов при разработке данного подхода были использованы фрактальный анализ и математическая статистика. Анализируются вопросы программной реализации предлагаемого подхода и формирования набора данных, содержащего сетевые пакеты компьютерных сетей. Экспериментальные результаты, полученные с использованием сгенерированного набораданных, продемонстрировали наличие самоподобия у сетевого трафика компьютерных сетей и подтвердили высокую эффективность предлагаемого подхода: он позволяет обнаруживать кибератаки в реальном или близком к реальному масштабе времени. The paper discusses an approach to detecting cyber attacks on computer networks, based on identifying anomalies in network traffic by assessing its self-similarity property. Methods for identifying long-term dependence in fractal Brownian motion and real network traffic of computer networks are considered. It is shown that the traffic of a telecommunication network is a self-similar structure and its behavior is close to fractal Brownian motion. Fractal analysis and mathematical statistics were used as tools in the development of this approach. The issues of the software implementation of the proposed approach and the formation of a data set containing network packets of computer networks are considered. The experimental results obtained using the generated dataset demonstrated the existence of selfsimilarity in the network traffic of computer networks and confirmed the fair efficiency of the proposed approach. The proposed can be used to quickly detect cyber attacks in real or near real time.

scholarly journals Basic characteristics of networks with self-similar traffic simulation

2019 ◽  
Vol 20 (1-2) ◽  
pp. 137-141
Author(s):  
Marek Aleksander ◽  
Roman Odarchenko ◽  
Sergiy Gnatyuk ◽  
Tadeusz Kantor
Keyword(s):  
Stochastic Process ◽  
Real Time ◽  
Experimental Research ◽  
Network Traffic ◽  
Network Performance ◽  
The Self ◽  
Self Similarity ◽  
Complex Models ◽  
Self Similar ◽  
Basic Characteristics

This paper is devoted to simulations the networks with self-similar traffic. The self-similarity in the stochastic process is identified by calculation of the Herst parameter value. Based on the results, received from the experimental research of network performance, we may conclude that the observed traffic in real-time mode is self-similar by its nature. Given results may be used for the further investigation of network traffic and work on the existing models of network traffic (particularly for new networks concepts like IoT, WSN, BYOD etc) from viewpoint of its cybersecurity. Furthermore, the adequacy of the description of real is achieved by complexifying the models, combining several models and integration of new parameters. Accordingly, for more complex models, there are higher computing abilities needed or longer time for the generation of traffic realization..

scholarly journals Estimation of Spectrum Requirements for Mobile Networks With Self-Similar Traffic, Handover, and Frequency Reuse

2010 ◽  
Vol 6 (4) ◽  
pp. 281-291
Author(s):  
Won Seok Yang ◽  
Eun Saem Yang ◽  
Hwa J. Kim ◽  
Dae K. Kim
Keyword(s):  
Mobile Networks ◽  
Probability Distributions ◽  
Frequency Reuse ◽  
Estimation Methods ◽  
Data Traffic ◽  
Load Spectrum ◽  
Self Similarity ◽  
Average Cell ◽  
Self Similar ◽  
The Impact

This paper considers self-similarity in data traffic, handover, and frequency reuse to estimate the spectrum requirements of mobile networks. An approximate average cell capacity subject to a delay requirement and self-similar traffic is presented. It is shown that handover traffic can be an additional load. Spectrum requirements are calculated based on carrier demand instead of spectral efficiency, as at least one carrier is necessary to transmit even 1 bit. The cell-split operation is considered under frequency reuse of one. Estimation methods are presented using cell traffic in two cases. First, a procedure is presented that estimates cell traffic from previous networks. Second, cell traffic is assumed to follow probability distributions. Numerical examples demonstrate the impact of self-similarity, handover, and the proportion of cell-split occurrences on the spectrum requirements.

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