Realtime classification for encrypted traffic

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

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء


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.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفExperimental Algorithms - 9th International Symposium, SEA 2010, Proceedings
عدد الصفحات13
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2010
الحدث9th International Symposium on Experimental Algorithms, SEA 2010 - Naples, إيطاليا
المدة: ٢٠ مايو ٢٠١٠٢٢ مايو ٢٠١٠

سلسلة المنشورات

الاسمLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
مستوى الصوت6049 LNCS
رقم المعيار الدولي للدوريات (المطبوع)0302-9743
رقم المعيار الدولي للدوريات (الإلكتروني)1611-3349


!!Conference9th International Symposium on Experimental Algorithms, SEA 2010


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