PDF Publication Title:
Text from PDF Page: 029
[67] [70] NSL-KDD - The authors report, employing AdaBoost with naive Bayes as weak learners, an F1 of 99.3% for a 23- labels scenario and an F1 of 98% for a 5-labels scenario; to obtain these figures they used 62,984 records for training (50% of NSL-KDD), where 53% are normal records and the remaining 47% are distributed among the different attack types; test results are based on 10-fold cross-validation over the training data, not on the test set. NSL-KDD - It explains why and how the NSL-KDD data set was created. They provide results of applying several methods to the NSL-KDD data. The best accuracy reported is 82.02% with Naive Bayes Tree using Weka. They use the full NSL_KDD dataset for training and testing, for the 2-labels prediction scenario. [68] NSL-KDD - It is applied a multilayer perceptron (MLP) with three layers to the NSL-KDD dataset, they achieved an accuracy of 79.9% for test data, for a 5-labels intrusion scenario. For a 2-labels (normal vs. anomaly) scenario they provided an accuracy of 81.2% for test data. [69] NSL-KDD Real data of private network - This work performs classification using a generative model based also in a Hidden Markov Model. This work reports a precision of 93.2% using their own dataset. - The authors report, for a 2-labels scenario and using self-organizing maps (SOM), a recall of 75.49% on NSL-KDD test data. [71] [72] NSL-KDD - The authors resorted to a deep belief network applied to the NSL-KDD dataset to do intrusion detection. They reported a detection accuracy of 97.5% using just 40% of the training data, but it is unclear what test dataset is used. [73] 1998 DARPA dataset (intrusion detection) - They employed continuous time bayesian networks as detection algorithm, using the 1998 DARPA dataset. They achieved good results on the 2-labels scenario; the metric provided is a ROC curve diagram, but no performance figures are given. [74] KDD99, NSL- KDD, Real traffic - This is a reference paper. It presents a survey of works related to machine learning architectures applied to NIDS, including generative models. [48] NSL-KDD - This paper explains the reasons for creating the NSL- KDD dataset. They give results for several algorithms. The best accuracy reported is 82.02% with naive Bayes tree using Weka. They use the full NSL_KDD dataset for training and testing, for the 2- labels scenario. [75] NSL-KDD - It proposes an approach to detect attacks using a deep auto encoder to perform dimensionality reduction before performing the classification of attacks. The algorithm used for classification is not mentioned. Using the deep autoencoder, the final accuracy on test data is 95.06%, with an unclear test dataset. The dimensionality reduction with the Doctoral Thesis: Novel applications of Machine Learning to NTAP - 27PDF 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 |