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Data Mining Techniques for Intrusion Detection on the Internet of Things Field

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International Conference on Cyber Security, Privacy and Networking (ICSPN 2022) (ICSPN 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 599))

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Abstract

Over the years, the Internet of Things (IoT) paradigm has acquired great importance due to various application possibilities. The need for Intrusion Detection System (IDS) arises related to the widespread of smart tools connected to each other. This paper aims to present a methodology based on data mining techniques to improve the protection of the connection in an Internet of Things application. In particular, this paper exploits machine learning techniques and Recommender Systems. The K-Nearest Neighbor method and a Context-Aware Recommender System allow the identification of attacks. A multiclassification module based on binary perceptron classifiers with a one-versus-one strategy allows the identification of the attack typology. The obtained numerical results are promising.

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Authors and Affiliations

  1. DIIn, University of Salerno, Fisciano, SA, Italy

    Marco Carratù, Francesco Colace, Angelo Lorusso, Antonio Pietrosanto, Domenico Santaniello & Carmine Valentino

Authors
  1. Marco Carratù
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  2. Francesco Colace
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  3. Angelo Lorusso
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  4. Antonio Pietrosanto
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  5. Domenico Santaniello
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  6. Carmine Valentino
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Corresponding author

Correspondence to Carmine Valentino .

Editor information

Editors and Affiliations

  1. Electronics Engineering and Telecom, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Nadia Nedjah

  2. University of Murcia, Murcia, Spain

    Gregorio Martínez Pérez

  3. Asia University, Taichung, Taiwan

    B. B. Gupta

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Carratù, M., Colace, F., Lorusso, A., Pietrosanto, A., Santaniello, D., Valentino, C. (2023). Data Mining Techniques for Intrusion Detection on the Internet of Things Field. In: Nedjah, N., Martínez Pérez, G., Gupta, B.B. (eds) International Conference on Cyber Security, Privacy and Networking (ICSPN 2022). ICSPN 2021. Lecture Notes in Networks and Systems, vol 599. Springer, Cham. https://doi.org/10.1007/978-3-031-22018-0_1

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