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Table 10 Summary of packet loss classification using offline training at end-systems of the network

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

Ref. ML Technique Network Dataset Features Classification Evaluation
       Settings Results
Liu et al. [282] Unsupervised: · EM for HMM Hybrid wired and wireless Synthetic data: · ns-2 simulation · 4-linear topology Data distribution: · Training = 10k · Loss pair RTT · Congestion loss · Wireless loss · 4-state HMM · Gaussian variables · Viterbi inference HMM accuracya: ·44−98%
Barman and Matta [38] Unsupervised: · EM for HMM Hybrid wired and wireless Synthetic data: · ns-2 simulation · Topology: - 4-linear - Dumbbell · Loss pair delay · Loss probabilities: - Congestion - Wireless (nw)nw: network support · Congestion loss · Wireless loss · 2-state HMM · Gaussian variables · Bayesian inference · Discretized values: - 10 symbols HMM accuracya: ·92−98%
El Khayat et al. [129, 130, 163] Supervised: · Boosting DT · DT · RF · Bagging DT · Extra-trees · MLP-NN ·k-NN Hybrid wired and wireless Synthetic data: · Simulation in: - ns-2 - BRITE ·> 1k random topologies Data distribution: · Training = 25k· Testing = 10k 40 features applying avg, stdev, min, and max on parameters: · One-way delay · IAT And on packets: · 3 following loss · 1 before loss · 1/2 before RTT [130] finds that adding the number of losses is insignificant · Congestion loss · Wireless loss Ensemble DT: · 25 trees NN: · 40 input neurons · 2 hidden layers with 30 neurons · 1 output neuron · LMAb learning k-NN: ·k=7 AUC (%)c: · 98.40 · 94.24 · 98.23 · 97.96 · 98.13 · 97.61 · 95.41
Fonseca and Crovella [150] Supervised: · Bayesian Wired Real data: · PMA project · BU Web server · Loss pair RTT · Congestion loss · Reordering · Gaussian variables · 0 to 3 historic samples In PMA: · TPR = 80% · FPR = 40% In BU: · TPR = 90% · FPR = 20%
Jayaraj et al. [214] Unsupervised: · EM for HMM · EM-clustering Optical Synthetic data: · ns-2 simulation · NSFNET topology Data distribution: · Training = 25k· Testing = 15k · Number of bursts between failures · Congestion loss · Contention loss HMM: · 8 states · Gaussian variables · Viterbi inference · 26 EM iterations Clustering: · 8 clusters · 24 EM iterations CVc: ·0.16−0.42·0.15−0.28 HMM accuracya: ·86−96%
  1. aVaries according to HMM prior estimates and network simulation settings (e.g. loss rate, error model, delay, traffic)
  2. bLevenberg-Marquardt Algorithm (LMA)
  3. cRespectively to the list of elements in the column ML technique