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The following table presents a summary of the main works related to the research carried out for this thesis. It provides a reference to the document, the data set used and the scope of the work. Objective/Area Ref. Dataset Scope Type of traffic classification [97] Internet traffic manually classified [102] - They propose a multi-layer perceptron (MLP) with one hidden layer, but it is actually adopted as the internal architecture to apply a fully Bayesian analysis. The best one vs. rest accuracy, using 246 features, for 10 grouped labels is 99.8%, and a macro averaged accuracy of 99.3% (10 labels). [98] TCP traces of backbone router of the University of Jinan - Classification with an ensemble of MLP classifiers with error-correcting output codes, achieving an average overall accuracy (for 5 labels) of 93.8%. [99] Auckland IV.: public available packet trace. -An MLP with a particle swarm optimization algorithm is employed to classify 6 labels with a best one vs. rest accuracy of 96.95%. [103] Moore dataset [104] - They use an MLP with 3 hidden layers and different numbers of hidden neurons, showing an overall accuracy greater than 96%, for a grouping of labels in 10 classes, resulting in a final class distribution very unbalanced (a frequency of almost 90% for highest frequency class), no F1 score is provided. [100] Moore dataset - They apply an MLP with 3 hidden layer combined with a fast correlation-based feature selection. [101] Data collected at the Florida Institute of Technology - A Parallel Neural Network Classifier Architecture is used. It is made up of parallel blocks of radial basis function neural networks. To train the network is employed a negative reinforcement learning algorithm. They classify 6 labels reporting a realistic overall accuracy of 95%, no F1 score is provided. [105] Traces collected at two backbone and two edge links located in the U.S., Japan, and Korea description length discretization of features as a preprocessing step to several algorithms: C4.5, Naïve Bayes, SVM and kNN. Claiming an enhanced performance of the algorithms, achieving a one vs. rest accuracy of 93.2%- 98% for 11 grouped labels. - This work proposes an entropy-based minimum [106] Proprietary network traffic captured with WireShark - Authors apply different machine learning techniques to NTC (C4.5, Support Vector Machine, Naïve Bayes) reporting an average accuracy of less than 80% using 23 features and detecting only five services (www, dns, ftp, p2p, and telnet) [107] Proprietary dataset - They employ an enhanced random forest with 29 selected features. They group the services in 12 classes, providing only one vs. rest metrics (not aggregated). Having F1 scores in the interval 0.3- 0.95, with only 3 classes higher than 0.96. [108] WIDE backbone dataset -This work includes flows correlation in a semi- supervised model providing overall accuracy of less than 85% and a one vs. rest F1 score, for 10 labels, Doctoral Thesis: Novel applications of Machine Learning to NTAP - 33PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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