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A Hybrid Deep Learning Ensemble for Cyber Intrusion Detection

2021; Springer International Publishing; Linguagem: Inglês

10.1007/978-3-030-80568-5_3

2661-815X

Anastasios Panagiotis Psathas, Lazaros Iliadis, Antoniοs Papaleonidas, Dimitris Bountas,

Internet Traffic Analysis and Secure E-voting

The daily growth of computer networks usage increases the need to protect users from malware and other threats. This paper, presents a hybrid Intrusion Detecting System (IDS) comprising of a 2-Dimensional Convolutional Neural Network (2-D CNN), a Recurrent Neural Network (RNN) and a Multi-Layer Perceptron (MLP) for the detection of 9 Cyber Attacks versus normal flow. The timely Kitsune Network attack dataset was used in this research. The proposed model achieved an overall accuracy of 92.66%, 90.64% and 90.56% in the train, validation and testing phases respectively. The typical five classification indices Sensitivity, Specificity, Accuracy, F1-Score and Precision were calculated following the "One-Versus-All Strategy". Their values clearly support the fact that the model can generalize and that it can be used as a prototype for further research on network security enhancement.