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Open AccessJournal ArticleDOI

Modeling and Analysis of Anomalies in the Network Infrastructure Based on the Potts Model

Andrzej Paszkiewicz
- 25 Jul 2021 - 
- Vol. 23, Iss: 8, pp 949
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TLDR
In this paper, the authors examined issues concerning the occurrence of anomalies affecting the process of phase transitions in network structures, particularly in IT networks, Internet of Things and Internet of Everything.
Abstract
The paper discusses issues concerning the occurrence of anomalies affecting the process of phase transitions. The considered issue was examined from the perspective of phase transitions in network structures, particularly in IT networks, Internet of Things and Internet of Everything. The basis for the research was the Potts model in the context of IT networks. The author proposed the classification of anomalies in relation to the states of particular nodes in the network structure. Considered anomalies included homogeneous, heterogeneous, individual and cyclic disorders. The results of tests and simulations clearly showed the impact of anomalies on the phase transitions in the network structures. The obtained results can be applied in modelling the processes occurring in network structures, particularly in IT networks.

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Journal ArticleDOI

Ising Model: Recent Developments and Exotic Applications

Adam Lipowski
- 01 Dec 2022 - 
TL;DR: In this paper , the one-dimensional version of a certain lattice model of ferromagnetism formulated by Lenz was solved in a one dimensional version of the lattice.
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A survey of network anomaly detection techniques

TL;DR: This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering and evaluates effectiveness of different categories of techniques.
Book

Phase transition dynamics

Akira Ōnuki
Journal ArticleDOI

A survey of deep learning-based network anomaly detection

TL;DR: An overview of deep learning methodologies, including restricted Bolzmann machine-based deep belief network, deep neural network, and recurrent neuralnetwork, as well as the machine learning techniques relevant to network anomaly detection are presented.
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