A new features vector matching for big heterogeneous data in intrusion detection context

Marwa Elayni (1) , Ouajdi Korbaa , Farah Jemili , Basel Solaiman (1)

1 IMT Atlantique - ITI - Département lmage et Traitement Information

Nowadays, the volume of data considerably increasing, the data is exploding on the scale of the Exabyte and the Zettabyte at an exceptionally high rate. These can be characterized as big data. Hence, the security of the network, Internet, websites, Iot devices and the organizations, of this growth is indispensable. Detecting intrusions in such a big heterogeneous data environment is challenging. In this paper, we will present a new representation of data that can support this big heterogeneous environment. We will use three different datasets and propose an automatically matching algorithm that measures the semantic similarity between each two features existing on different datasets. Thereafter, an approximate vector is created that any type of coming data can be stored. With this representation, we can have subsequently an efficient intrusion detection system that can be able to acknowledge any instance of the existing data in the networks.

Mots clés: Big heterogeneous data; intrusion detection systems; semantic similarity; matching data; Jaccard similarity coefficient

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Environnements Informatiques pour l'Apprentissage Humain

Marwa Elayni, Ouajdi Korbaa, Farah Jemili, Basel Solaiman. A new features vector matching for big heterogeneous data in intrusion detection context. ATSIP 2020 : 5th International Conference on Advanced Technologies for Signal and Image Processing, Sep 2020, Sousse, Tunisia. pp.1-5, ⟨10.1109/ATSIP49331.2020.9231671⟩. ⟨hal-02989384⟩