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2020 | OriginalPaper | Chapter

MLP4NIDS: An Efficient MLP-Based Network Intrusion Detection for CICIDS2017 Dataset

Authors : Arnaud Rosay, Florent Carlier, Pascal Leroux

Published in: Machine Learning for Networking

Publisher: Springer International Publishing

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Abstract

More and more embedded devices are connected to the internet and therefore are potential victims of intrusion. While machine learning algorithms have proven to be robust techniques, it is mainly achieved with traditional processing, neural network giving worse results. In this paper, we propose usage of a multi-layer perceptron neural network for intrusion detection and provide a detailed description of our methodology. We detail all steps to achieve better performances than traditional machine learning techniques with a detection of intrusion accuracy above 99% and a low false positive rate kept below 0.7%. Results of previous works are analyzed and compared with the performances of the proposed solution.

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Title: MLP4NIDS: An Efficient MLP-Based Network Intrusion Detection for CICIDS2017 Dataset
Authors: Arnaud Rosay
Florent Carlier
Pascal Leroux
Publisher: Springer International Publishing
Book: Machine Learning for Networking
Print ISBN: 978-3-030-45777-8

Electronic ISBN: 978-3-030-45778-5

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-45778-5_16

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