PUBLISHED PAPERS
2025 |
AIN’T - An Artificial Intelligent Network Thermometer for Measurements of Link Saturation on TCP/IP Flows Proceedings Article Marcelo R. Silva; Cesar Marcondes Resumo | Links | BibTeX | Tags: Deep Learning, Passive network monitoring, Transmission Control Protocol (TCP) @inproceedings{Silva2025AintAn,The transmission capacity of data links is crucial for network administrators. This measure is particularly significant in operational environments where maintaining communication continuity is vital. However, the principal strategy of the most widely used tools or protocols for this purpose consists of inserting extra packets into the network and throttling its transmission capacity. Such an active strategy has the potential, even momentarily, to produce packet losses in combat support applications (SAD, for example) and crash communications on the network under analysis. Seeking to avoid network overload while measuring its saturation, this work proposes AIN’T (Artificial Intelligent Network Thermometer). AIN’T measures the level of congestion on the data link passively without inserting any data packets into the respective infrastructure. To this end, it applies MLP, LSTM, and CNN Deep Learning Networks. The results show that the models extracted from these neural network architectures can distinguish between high and low-level link saturation in an IP data network with over 99% precision. |