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

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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https://hal.inria.fr/hal-03266466
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Submitted on : Monday, June 21, 2021 - 5:32:02 PM
Last modification on : Wednesday, April 27, 2022 - 4:39:44 AM
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Arnaud Rosay, Florent Carlier, Pascal Leroux. MLP4NIDS: An Efficient MLP-Based Network Intrusion Detection for CICIDS2017 Dataset. 2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.240-254, ⟨10.1007/978-3-030-45778-5_16⟩. ⟨hal-03266466⟩

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