22nd EANN 2021, 25 - 27 June 2021, Greece

A Hybrid Deep Learning Ensemble for Cyber Intrusion Detection

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


  The daily growth of computer networks usage increases the need to protect us-ers from malware and other threats. This paper, presents a hybrid Intrusion De-tecting 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 pro-posed model achieved an overall accuracy of 92.66%, 90.64% and 90.56% in the train, validation and testing phases respectively. The typical five classifica-tion indices Sensitivity, Specificity, Accuracy, F1-Score and Precision were calculated following the “One-Versus-All Strategy”. Their values clearly sup-port the fact that the model can generalize and that it can be used as a prototype for further research on network security enhancement.  

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