Smart topology detection using multi-armed bandit reinforcement learning method

Abstract

ABSTRACT Inventory management in network infrastructures currently relies on static methods, leading to data consistency issues due to the difficulty of keeping static reports and lists up to date with the dynamic nature of real-world networks. An alternative approach is the use of automatically generated network topology diagrams, which provide a visual representation of the network’s structure and organization, depicting the configuration and interconnections of nodes and links. These diagrams serve purposes such as network planning, management, and security management. However, the creation of automatic network topology diagrams remains a challenge. This paper addresses this challenge by proposing a Reinforcement Learning-based approach that utilizes network traffic data to automatically generate network topology diagrams. The preliminary results demonstrate promising outcomes, enabling the automatic generation of efficient network topology diagrams for various purposes, even in the case of large-scale networks. These automatically created diagrams can further facilitate security analysis, planning, and optimization activities. By focusing on enhancing security outcomes and enabling robust network analysis, this research contributes to advancing the field of network management, particularly in the domain of automatic network visualization and comprehensive security analysis.