Port2dist: Semantic port distances for network analytics

Laurent Evrard, Jerome Francois, Jean Noel Colin, Frederic Beck

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

Résumé

Traffic analysis is a predominant task to support multiple types of management operations. When shifting from manually built signatures to machine learning techniques, a problem resides in the model to represent traffic features. The most notable examples are the TCP and UDP ports, near port numbers in the numerical space is not representative of a close semantic from an operational point of view. We have thus developed a technique to learn meaningful metrics between ports from scanning strategies followed by attackers. In this demonstration, we propose the port2dist tool, allowing to get, seek and retrieve semantic dissimilarities between port numbers.

langue originaleAnglais
titre2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019
EditeurInstitute of Electrical and Electronics Engineers Inc.
Pages747-748
Nombre de pages2
ISBN (Electronique)9783903176157
Etat de la publicationPublié - 16 mai 2019
Evénement2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019 - Arlington, États-Unis
Durée: 8 avr. 201912 avr. 2019

Série de publications

Nom2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019

Une conférence

Une conférence2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019
PaysÉtats-Unis
La villeArlington
période8/04/1912/04/19

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