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

Area of Science:

• Complex Systems Science
• Network Theory
• Information Technology

Background:

• Phase transitions are critical phenomena in various systems, including network structures.
• Understanding anomalies is essential for reliable operation of Information Technology (IT) networks, Internet of Things (IoT), and Internet of Everything (IoE).

Purpose of the Study:

• To investigate the impact of anomalies on phase transition processes within network structures.
• To classify anomalies based on node states in IT networks.
• To analyze the influence of different anomaly types on network behavior.

Main Methods:

• Utilized the Potts model adapted for IT network contexts.
• Classified anomalies into homogeneous, heterogeneous, individual, and cyclic types.
• Conducted tests and simulations to observe anomaly effects.

Main Results:

• Anomalies significantly affect phase transition dynamics in network structures.
• The classification of anomalies provides a framework for understanding their specific impacts.
• Simulation results demonstrate a clear correlation between anomaly presence and phase transition alterations.

Conclusions:

• Anomalies play a critical role in modulating phase transitions within IT, IoT, and IoE networks.
• The proposed anomaly classification and findings are valuable for network process modeling.
• This research contributes to the robustness and predictive modeling of complex network systems.