Mathematical Representation of Netflow Analysis Decision Making Based on Production Logic

In the sphere of NetFlow traffic analysis, accurate detection of anomalous behavior in real time remains a critical challenge. This study will present a mathematical representation for decision making in NetFlow analysis, production implementation logic for automated expert knowledge. A specialized software system will be developed for collecting and processing NetFlow traffic events in real time cyberspace. NetFlow data will be accumulated in PCAP (Packet Capture) format and converted to .csv using a three-step algorithmic sequence: packet reading, key feature extraction, and output formatting. A total of 89 different packet features will be identified, grouped into 10 categories, including flow statistics, payload features, inter-arrival time, and TCP flags. Based on these features, 110 frequently occurring network processes will be synthesized and identified into seven sets, such as classic cyber threats, application-level cyber threats, anomalies, and normal netflows. Each event will be formally expressed using Boolean production rules in the IF...THEN format, linking subsets of feature vectors (F) to certain network events (A). These production rules form the knowledge base of the expert system, which allows for the efficient identification of cyber threats such as DDoS attacks, port scanning, spoofing, and covert channels. The architecture will ensure systematic analysis for the early detection and identification of netflow anomalies, facilitating to the solidity and protection of complex information systems. The suggested decision-making representation based on production logic will ensure scalable and explainable synthesis and/or analysis, opening the way to the creation of intelligent structures for identifying cyber threats in cyberspace. The accuracy of the results obtained was checked using a confusion matrix.