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Improving threat detection in networks using deep learning

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Abstract

Detecting threats on the Internet is a key factor in maintaining data and information security. An intrusion detection system tries to prevent such attacks from occurring through the analysis of patterns and behavior of the data stream in the network. This paper presents a large data stream detection and analysis distributed platform, through the use of machine learning to dimensionality reduction. The system is evaluated based on three criteria: the accuracy, the number of false positives, and number of false negatives. Each classifier presented better accuracy when using 5 and 13 features, having fewer false positives and false negatives, allowing the detection of threats in real-time over a large volume of data, with greater precision.

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Authors and Affiliations

  1. Universidade Tecnólogica Federal do Paraná, Curitiba, PR, Brazil

    Fábio César Schuartz, Mauro Fonseca & Anelise Munaretto

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  1. Fábio César Schuartz
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  2. Mauro Fonseca
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  3. Anelise Munaretto
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Correspondence to Fábio César Schuartz.

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Schuartz, F.C., Fonseca, M. & Munaretto, A. Improving threat detection in networks using deep learning. Ann. Telecommun. 75, 133–142 (2020). https://doi.org/10.1007/s12243-019-00743-5

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