An Algorithm for Selecting an Optimal Classification Method for Computer Network Traffic

This paper addresses the development of an algorithm for selecting an efficient method for classifying objects in computer network traffic. The study analyzes rule-based packet classification, matching types, including exact, prefix, and range matching, as well as the priorities assigned to these rules. The performance of various classification algorithms is evaluated in terms of computational time and memory consumption, and the algorithms are categorized into four main approaches. In the course of the study, Tuple Space Search, Hierarchical Tries, HyperCuts, and Aggregate Bit Vector algorithms are selected, and their suitability and efficiency for real-time systems are substantiated. Furthermore, the developed algorithm enables multi-criteria analysis of network packets, determination of the final class based on rule priorities, and detection of anomalies in network traffic.