Towards a Hierarchical Deep Learning Approach for Intrusion Detection

Francois Alin (1) , Amine Chemchem (1) , Florent Nolot (1) , Olivier Flauzac (1) , Michaël Krajecki (1)

1 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804

Nowadays, it is almost impossible to imagine our daily life without Internet. This strong dependence requires an effective and rigorous consideration of all the risks related to computer attacks. However traditional methods of protection are not always effective, and usually very expensive in treatment resources. That is why this paper presents a new hierarchical method based on deep learning algorithms to deal with intrusion detection. This method has proven to be very effective across traditional implementation on four public datasets, and meets all the other requirements of an efficient intrusion detection system.

Domaines: Intelligence artificielle [cs.AI]; Cryptographie et sécurité [cs.CR]; Apprentissage [cs.LG]

Francois Alin, Amine Chemchem, Florent Nolot, Olivier Flauzac, Michaël Krajecki. Towards a Hierarchical Deep Learning Approach for Intrusion Detection. International Conference on Machine Learning for Networking (MLN), 2019, Paris, France. ⟨10.1007/978-3-030-45778-5_2⟩. ⟨hal-02560294⟩