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.

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

Colace F.
;
Lorusso A.;Pietrosanto A.;Santaniello D.;Valentino C.
2023-01-01

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.
2023
978-3-031-22017-3
978-3-031-22018-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4819535
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