A Hybrid Deep Learning Ensemble for Cyber Intrusion Detection

Autor:	Antonios Papaleonidas, Anastasios Panagiotis Psathas, Lazaros Iliadis, Dimitris Bountas
Rok vydání:	2021
Předmět:	Computer science Network security business.industry Deep learning Intrusion detection system Perceptron computer.software_genre Machine learning Convolutional neural network Recurrent neural network Malware Sensitivity (control systems) Artificial intelligence business computer
Zdroj:	Proceedings of the International Neural Networks Society ISBN: 9783030805678 EANN
DOI:	10.1007/978-3-030-80568-5_3
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a0170d04c5a945ac1b6266c323c0d59d https://doi.org/10.1007/978-3-030-80568-5_3 Zobrazit plný text záznamu