A study on comparison analysis of the dnn, cnn, and rnn models for network anomaly detection

Abstract

 With the widespread use of the Internet, network technology is used in a large amount in dailylife, and the Internet and network are currently suffering from severe threats of network attacks.Network anomaly detection is one of the most significant issues in network security, and it is a coremethod to prevent cyber-attacks because it monitors network traffic data to figure out whether they are normal or abnormal. A variety of research frameworks have been proposed for network anomalydetection, and nowadays, deep learning-based methodologies are in the spotlight. For this reason, thisresearch employed three deep learning models, i.e., Deep Neural Network (DNN), Convolutional NeuralNetwork (CNN), and Recurrent Neural Network (RNN) models, with the public dataset, which is CICIDS2017 dataset to examine their effectiveness for network anomaly detection. After evaluating the threedeep learning models with the CICIDS 2017 dataset, the experimental results show that all three deepScore. This means that they could facilitate a more in-depth analysis of network data and identifyanomalies faster. Besides, we observed that the DNN model outperformed the other two deep learningmodels, which achieved 98.14% of the overall detection accuracy. It proves that deep leaning models
seem to be a robust potential tool for network anomaly detection in the cybersecurity field.