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Table 4 Summary of Payload ⋆ and Host Behavior †-based Traffic Classification

From: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Ref.

ML Technique

Dataset

Features

Classes

Evaluation

     

Settings

Results

Haffner et al. [176] ⋆

Supervised NB, AdaBoost, MaxEnt

Proprietary

Discrete byte encoding for first n bytes of unidirectional flow

FTP, SMTP, POP3, IMAP, HTTPS, HTTP, SSH

n=64−256 bytes

Overall error rate <0.51%, precision > 99%, recall > 94%

Ma et al. [286] ⋆

Unsupervised HCA

Proprietary: U. Cambridge, UCSD

Discrete byte encoding for first n bytes of unidirectional flow

FTP, SMTP, HTTP, HTTPS, DNS, NTP, NetBIOS, SrvLoc

n=64 bytes, distance metric: PD = 250, MP = 150, CSG = 12%

Error rate: PD ≤ 4.15%, MP ≤ 9.97%, CSG ≤ 6.19%

Finamore et al. [146] ⋆

Supervised SVM

Tstat [439]; NAPA-WINE [268]; Proprietary: ISP network

Statistical characterization of first N bytes of each packet a window of size C, divided into G groups of b consecutive bits

eMule, BitTorrent, RTP, RTCP, DNS, P2P-TV (PPLive, Joost, SopCast, TVAnts), Skype, Background

C=80,N=12,G=24,b=4

Average TP = 99.6%, FP < 1%

Schatzmann et al. [404] †

Supervised SVM

Proprietary: ISP network

Service proximity, activity profiles, session duration, periodicity

Mail, Non-Mail

N/A

Average accuracy = 93.2%, precision = 79.2%

Bermolan et al. [53] †

Supervised SVM

Proprietary: campus network, ISP network

Packet count exchanged between peers in duration â–³T

PPLive, TVAnts, SopCast, Joost

â–³T=5 s, SVM distance metric R=0.5

Worst-case TPR ≈95%, FPR < 0.1%

  1. N/A: Not available