PDF Publication Title:
Text from PDF Page: 115
the work performed. Section IV describes the results obtained and finally, Section V provides discussion and conclusions. II. RELATED WORKS Comparison of work results is difficult in NTC because the datasets being studied and the performance metrics applied are very different. NTC is intrinsically a multi-class classification problem. There is no single universally agreed metric to present results in a multi-class problem, as will be shown in Section IV. Considering these facts, we now present several related works. There are many works that apply neural networks to NTC, but the network models employed are very different in nature to the ones presented here. In [5] they propose a multi-layer perceptron (MLP) with zero or 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). An ensemble of MLP classifiers with error-correcting output codes is applied in [6], achieving an average overall accuracy (for 5 labels) of 93.8%. Meanwhile, in [7] an MLP with a particle swarm optimization algorithm is employed to classify 6 labels with a best one vs. rest accuracy of 96.95%. Somehow related, the purpose of [8] is to investigate neural projection techniques for network traffic visualization. Towards that end, they propose several dimensionality reduction methods using neural networks. No classification is performed. Another work [9] explores the applicability of rough neural networks to deal with uncertainty but does not give any performance results for NTC. Zhou et al. [10] apply an MLP with 3 hidden layers and different numbers of hidden neurons to the Moore dataset [11]. They give 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. A Parallel Neural Network Classifier Architecture is used in [12]. 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. Another set of papers applies general machine learning techniques, not related with neural networks, to the NTC problem. Kim et al. [13] propose an entropy-based minimum 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. In [14] 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) Wang et al. [15] 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. They use their own dataset. Authors of [16] include flows correlation in a semi-supervised model providing overall accuracy of less than 85% and a one vs. rest F1 score, for 10 labels, of less than 0.9 (except two labels with 0.95 and 1). They use the WIDE backbone dataset [16]. They report having better results than other works using C4.5, kNN, Naïve Bayes, Bayesian Networks and Doctoral Thesis: Novel applications of Machine Learning to NTAP - 113PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
PDF Search Title:
Novel applications of Machine Learning to Network Traffic AnalysisOriginal File Name Searched:
456453_1175348.pdfDIY PDF Search: Google It | Yahoo | Bing
Cruise Ship Reviews | Luxury Resort | Jet | Yacht | and Travel Tech More Info
Cruising Review Topics and Articles More Info
Software based on Filemaker for the travel industry More Info
The Burgenstock Resort: Reviews on CruisingReview website... More Info
Resort Reviews: World Class resorts... More Info
The Riffelalp Resort: Reviews on CruisingReview website... More Info
| CONTACT TEL: 608-238-6001 Email: greg@cruisingreview.com | RSS | AMP |