PDF Publication Title:
Text from PDF Page: 066
redundant samples in KDD 99, being more useful and realistic. NSL-KDD provides a sufficiently large number of samples. The distribution of samples among intrusion classes (labels) is quite unbalanced and provides enough variability between training and test data to challenge any method that tries to reproduce the structure of the data. Fig. 19. Overview of datasets used in each paper In Fig. 20 are shown the different main models proposed in the papers. In addition to these models, other models have been considered not as part of the main research activity, but to provide a comparison of results. For example, in [3], the results obtained from the C-VAE model are compared with the results of several classic machine learning models such as Random Forest, Support Vector Machine (SVM), Logistic Regression and Multilayer Perceptron (MLP). Similarly, in [5] the synthetic data generated by the proposed model is compared with other over-sampling algorithms: SMOTE, SMOTE Borderline, SMOTE ENN, SMOTE Tomek, SMOTE SVM, Easy Ensemble and ADADSYN. In this case to compare the properties of the synthetic data we also needed to use several ML algorithms to check with them that the new data was able to improve the algorithms training; for this task we used 4 well-known ML algorithms: random forest, MLP, logistic regression and SVM. Most of the algorithms proposed in this thesis and mentioned in Fig. 17 are deep learning algorithms. For example, in [2] we use a combination of CNN and LSTM (a variant of RNN) networks to detect the type-of-service of a network flow; and, in [3], we propose a variant of a VAE which is called a conditional VAE (C-VAE) to perform prediction and generate synthetic features for intrusion detection. In [4] we also propose a final classifier based on a combination of CNN and LSTM networks. In all these cases, it has been a challenge the application of deep learning algorithms: Doctoral Thesis: Novel applications of Machine Learning to NTAP - 64PDF 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 (Standard Web Page)