Realtime classification for encrypted traffic

Roni Bar-Yanai, Michael Langberg, David Peleg, Liam Roditty

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים


Classifying network flows by their application type is the backbone of many crucial network monitoring and controlling tasks, including billing, quality of service, security and trend analyzers. The classical "port-based" and "payload-based" approaches to traffic classification have several shortcomings. These limitations have motivated the study of classification techniques that build on the foundations of learning theory and statistics. The current paper presents a new statistical classifier that allows real time classification of encrypted data. Our method is based on a hybrid combination of the k-means and knearest neighbor (or k-NN) geometrical classifiers. The proposed classifier is both fast and accurate, as implied by our feasibility tests, which included implementing and intergrading statistical classification into a realtime embedded environment. The experimental results indicate that our classifier is extremely robust to encryption.

שפה מקוריתאנגלית
כותר פרסום המארחExperimental Algorithms - 9th International Symposium, SEA 2010, Proceedings
מספר עמודים13
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 2010
אירוע9th International Symposium on Experimental Algorithms, SEA 2010 - Naples, איטליה
משך הזמן: 20 מאי 201022 מאי 2010

סדרות פרסומים

שםLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
כרך6049 LNCS
ISSN (מודפס)0302-9743
ISSN (אלקטרוני)1611-3349


כנס9th International Symposium on Experimental Algorithms, SEA 2010

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