Learning to Detect Network Intrusion from a Few Labeled Events and Background Traffic - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

Learning to Detect Network Intrusion from a Few Labeled Events and Background Traffic

Résumé

Intrusion detection systems (IDS) analyse network traffic data with the goal to reveal malicious activities and incidents. A general problem with learning within this domain is a lack of relevant ground truth data, i.e. real attacks, capturing malicious behaviors in their full variety. Most of existing solutions thus, up to a certain level, rely on rules designed by network domain experts. Although there are advantages to the use of rules, they lack the basic ability of adapting to traffic data. As a result, we propose an ensemble tree bagging classifier, capable of learning from an extremely small number of true attack representatives, and demonstrate that, incorporating a general background traffic, we are able to generalize from those few representatives to achieve competitive results to the expert designed rules used in existing IDS Camnep.
Fichier principal
Vignette du fichier
978-3-319-20034-7_9_Chapter.pdf (326.53 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01410151 , version 1 (06-12-2016)

Licence

Identifiants

Citer

Gustav Šourek, Ondřej Kuželka, Filip Železný. Learning to Detect Network Intrusion from a Few Labeled Events and Background Traffic. 9th Autonomous Infrastructure, Management, and Security (AIMS), Jun 2015, Ghent, Belgium. pp.73-86, ⟨10.1007/978-3-319-20034-7_9⟩. ⟨hal-01410151⟩
62 Consultations
108 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More