Principles of Modeling in Information Communication Systems and Networks

Abstract

The authors present in this entry chapter the basic rubrics of models, modeling, and simulation, an understanding of which is indispensible for the comprehension of subsequent chapters of this text on the all-important topic of modeling and simulation in Information Communication Systems and Networks (ICSN). A good example is the case of analyzing simulation results of traffic models as a tool for investigating network behavioral pattarns as it affects the transmitted content (Atayero, et al., 2013). The various classifications of models are discussed, for example classification based on the degree of semblance to the original object (i.e. isomorphism). Various fundamental terminologies without the knowledge of which the concepts and models and modeling cannot be properly understood are explained. Model stuctures are highlighted and discussed. The methodological basis of formalizing complex system structures is presented. The concept of componential approach to modeling is presented and the necessary stages of mathematical model formation are examined and explained. The chapter concludes with a presentation of the concept of simulation vis-à-vis information communication systems and networks.

Wavelet-analysis of network traffic time-series for detection of attacks on digital production infrastructure

“…Its idea consists in the fact that any digital sequence is represented as a combination of decomposition coefficients as per basic scaling and wavelet-functions [5]. Thus, wavelettransform decomposes the analyzed sequence into two sequences of coefficients: approximation coefficients , which are linked with a scaling function and detailing coefficients , which are linked with wavelet-function.…”

“…It means that it is necessary to detect change points caused by a rapid change in dispersion of detailing coefficients in order to detect abnormalities in local traffic trend. In a number of works it is offered to use ICSS criteria (iterative algorithm of cumulative sum of squares) [7,8], SIC (Shwartz information criterion) [9], Fisher criterion making it possible to trace dispersion equality [5] and etc. This paper offers to use a Bayesian on-line change detection algorithm [10].…”