Classification of Simulated Fake Bandwidth Data Using LSTM

Abstract

Hardly will someone acknowledge that the bandwidth we use every day is as authentic as most ISPs advertise, even those offering dedicated services. There are usually shortcomings, especially on upload and download bandwidth speeds. This paper presents the classification of simulated fake bandwidth data using the Long Short-Term Memory model, which though seldom found, is a very effective approach in network analysis. There were 1400 bandwidth data points collected from the MikroTik RB 1100 AHx device in a month, then further processed with normalization, and divided to have 80% training and 20% testing. The LSTM model applied had an accuracy rate of 98.93%, proving that it is capable of classifying either genuine or fake bandwidth instances accordingly. Of 1,400 test data points, the model managed to classify 723 as fake bandwidth and another 677 as genuine, resulting in a classification error rate of only 1.07%. The results clearly prove that LSTM has huge potential for real-time bandwidth manipulation detection, key to enhancing trust and efficiency in network management. In this respect, this research shows that bandwidth analysis combined with LSTM can be an original solution for network monitoring