Novel applications of Machine Learning to Network Traffic Analysis

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Novel applications of Machine Learning to Network Traffic Analysis ( novel-applications-machine-learning-network-traffic-analysis )

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of less than 0.9 (except two labels with 0.95 and 1). They report having better results than other works using C4.5, kNN, Naïve Bayes, Bayesian Networks and Erman ́s semi-supervised methods. [109] Moore dataset [104] - A Directed Acyclic Graph-Support Vector Machine is proposed in this work, attaining an average accuracy of 95.5%. The method is applied to a one- to-one combination of classes [110] UNB ISCX Network Traffic dataset [111] - They study the application of several algorithms: J48, Random Forest, Bayes Net, and kNN to UNB ISCX Network Traffic dataset, with 14 classes and 12 features, reporting a best classification accuracy of 93.94% for the kNN algorithm. [112] -This work presents a variant of decision tree algorithm C4.5 working on the Hadoop platform. They classify 12 labels giving a one vs. rest accuracy in the interval 60-90% for all the labels with only two labels with a value higher than 90%. Moore dataset [104] [113] Traces from the Internet Link of the University of Calgary - This work presents Erman ́s semi-supervised method. This method consists in clustering the flows using K-Means or some alternative clustering method and then mapping the clusters centroids to traffic types using Euclidean distance. An accuracy greater than 90% is reported. Table 3. Type of traffic prediction - related works Doctoral Thesis: Novel applications of Machine Learning to NTAP - 34

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