Methodology of probabilistic analysis of state dynamics of multi­dimensional semi­Markov dynamic systems

Meleshko, Yelyzaveta; Раскін, Лев; Semenov, Serhii; Сіра, Оксана

doi:10.15587/1729-4061.2019.184637

Cited by 6 publications

(11 citation statements)

References 7 publications

(9 reference statements)

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“…The developed model contains a set of integral equations with respect to unknown functions that describe the probable dynamics of a system (1). The development of this mathematical model was based on the method for probabilistic analysis of dynamics of the states of multidimensional semi-Markovian dynamic systems, proposed in [24]. The equations (1) were solved with the help of Laplace transform, with the use of which the integral equations were replaced with the system of linear algebraic equations (2) that has the solution (3).…”

Section: Discussion Of Results Of the Development Of A Model Of Dynammentioning

confidence: 99%

“…With the use of representations for densities of transition distributions, the expressions for the Laplace transformants of conditional probabilities of transitions from state H 1 (4) were obtained. Unlike the example for two possible states [24], in this case, the reverse transition is quite cumbersome (since it is necessary to use formulas for the roots of algebraic equations of third and fourth powers). That is why the transition to the originals for conditional probabilities is performed for specific numerical values of parameters.…”

Section: Discussion Of Results Of the Development Of A Model Of Dynammentioning

confidence: 99%

“…The methodology of probabilistic analysis of the dynamics of states of multidimensional Markovian and semi-Markovian dynamical systems, developed in paper [24], was used to develop a mathematical model of dynamics of states of a recommendation system.…”

Section: Development Of a Mathematical Model Of The Dynamics Of A Recmentioning

confidence: 99%

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Developing a model of the dynamics of states of a recommendation system under conditions of profile injection attacks

Meleshko¹,

Drieiev²,

Yakymenko³

et al. 2020

EEJET

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The recommendation systems used to form a news feed in social networks or to create recommendation lists on content websites or Internet stores are often exposed to information profile injection attacks. These attacks are aimed at changing ratings, and thus at changing the frequency of appearing in recommendations, certain objects of a system. This can lead to threats to users' information security and losses of the system owners. There are methods to detect attacks in recommendation systems, but they require permanent repetitive checks of all users' profiles, which is a rather resource-intensive operation. At the same time, these methods do not contain any proposals as for determining the optimal frequency of attack checks. However, a properly chosen frequency of such checks will not overload a system too much and, at the same time, will provide an adequate level of its operational security. A mathematical model of the dynamics of states of a recommendation system under conditions of an information attack with the use of the mathematical apparatus of Markovian and semi-Markovian processes was developed. The developed model makes it possible to study the influence of profile injection attacks on recommendation systems, in particular, on their operation efficiency and amount of costs to ensure their information security. The practical application of the developed model enables calculating for recommendation systems the optimum frequency of information attack check, taking into consideration the damage from such attacks and costs of permanent inspections. Based on the developed mathematical model, the method for determining total costs of a recommendation system as a result of monitoring its own information security, neutralization of bot-networks activity and as a result of information attacks was proposed. A method for determining the optimal frequency of checking a recommendation system for information attacks to optimize the overall costs of a system was developed. The application of this method will enable the owners of websites with recommendation systems to minimize their financial costs to provide their information security

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Section: Discussion Of Results Of the Development Of A Model Of Dynammentioning

confidence: 99%

Section: Discussion Of Results Of the Development Of A Model Of Dynammentioning

confidence: 99%

See 1 more Smart Citation

Developing a model of the dynamics of states of a recommendation system under conditions of profile injection attacks

Meleshko¹,

Drieiev²,

Yakymenko³

et al. 2020

EEJET

Self Cite

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“…Traffic is generated with fractal properties [5]. Traffic generation is based on the theory of Markov processes, which is often used to model the traffic of various mass service systems [6][7][8][9][10][11][12].…”

Section: Main Materialsmentioning

confidence: 99%

“…Network traffic has fractal properties and can be modeled using fractal dimension and Markov processes [5]. Therefore, the generation of traffic to reproduce its fractal properties is based on the theory of Markov processes, which is often used to model the traffic of various mass service systems [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Computer Simulation Model of a Computer Network With Fractal Traffic for Testing Routing Algorithms

Hanna

Meleshko²,

Drieiev³

et al. 2022

A.I.S.

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The object of research in this article is simulation modeling of a computer network and the process of traffic routing. The relevance of the research is due to the importance of ensuring the quality of service in computer networks, in particular, by necessity reducing the number of lost IP-packets at high traffic intensity values. Determining the routing of traffic packets is a complex process and is based on various indicators or combinations of indicators. If the routing process takes place in a dynamic mode, then the complexity of the route calculation increases, in this case, one of the tools for research and comparison of different routing algorithms can be a computer simulation model of a computer network. The goal of the work is the development and research of a computer simulation model of a computer network for testing traffic routing algorithms. The tasks: to develop computer simulation model of a computer network to generate the network structure and simulate the traffic distribution process with the ability to test different routing algorithms. Research methods: theory of computer networks, theory of fractal analysis, object-oriented programming, theory of algorithms and data structures, theory of complex networks, theory of Markov processes. Conclusions. The paper investigated the basic principles of traffic routing in computer networks. A simulation model of a computer network for testing traffic routing algorithms has been developed. A method based on the theory of complex networks has been developed to generate the structure of a computer network. Theory of fractal analysis and Markov processes are used for traffic generation. A series of experiments was conducted on a developed model to determine how different fractal dimensions of traffic at high traffic intensity values affect the number of lost packets, and therefore the quality of service. Analyzing the results of the experiment, the following conclusions can be drawn: the least number of lost packets occurs when the process is random or has weak trends. The fewest lost packets were at fractal dimension 1.5, i.e., when the process is completely random, there were also few lost packets at fractal dimensions close to this; persistent and anti-persistent processes (those with memory) cause more packet loss for the same traffic intensity and maximum number of packets sent from one device per unit of time. Moreover, anti-persistent processes cause significantly more losses than persistent ones. Thus, when performing traffic routing and finding optimal paths for sending IP-packets, it can be useful to determine and take into account the fractal dimension of traffic at the entrance of each router and use it when calculating metrics to determine the best routes.

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Model of the Dynamics of the State of Educational Content Recommender System

Muchacki,

Sitnikova,

Yenhalychev

2023

2023 13th International Conference on Dependable Systems, Services and Technologies (DESSERT)

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Methodology of probabilistic analysis of state dynamics of multidimensional semiMarkov dynamic systems

Cited by 6 publications

References 7 publications

Developing a model of the dynamics of states of a recommendation system under conditions of profile injection attacks

Developing a model of the dynamics of states of a recommendation system under conditions of profile injection attacks

Computer Simulation Model of a Computer Network With Fractal Traffic for Testing Routing Algorithms

Model of the Dynamics of the State of Educational Content Recommender System

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Methodology of probabilistic analysis of state dynamics of multi­dimensional semi­Markov dynamic systems

Cited by 6 publications

References 7 publications

Developing a model of the dynamics of states of a recommendation system under conditions of profile injection attacks

Developing a model of the dynamics of states of a recommendation system under conditions of profile injection attacks

Computer Simulation Model of a Computer Network With Fractal Traffic for Testing Routing Algorithms

Model of the Dynamics of the State of Educational Content Recommender System

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Product

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Methodology of probabilistic analysis of state dynamics of multidimensional semiMarkov dynamic systems