Intrusion Detection Using Multilayer Perceptron and Neural Networks with Long Short-Term Memory
Б. Б. Борисенко, S. D. Erokhin, А. С. Фадеев, I. D. Martishin
Abstract
The article deals with the architecture and structure of the most effective artificial neural networks (ANNs) in the tasks of network traffic classification: multilayer perceptron and LSTM (long short-term memory) network. Using one of the actual datasets CSE-CIC-IDS2018 trained ANNs and tested the effectiveness of detection of various attacks in the network traffic. An analysis of existing work has been performed. A multiclass classification was performed and the detection effectiveness of separate computer attacks was reported. The effectiveness evaluation focused on three metrics: completeness, correct response rate, and false positive rate. The test classification results were considered both in terms of comparison of numerical characteristics of recognition quality and in terms of class analysis in order to identify which computer attacks (CA) are better identified using multilayer perceptron and which are better identified using LSTM. On the basis of these results, an ANN, which is a hybrid network, is proposed as a promising Intrusion Detection System (IDS).