PDF Publication Title:
Text from PDF Page: 163
PAPER 5 Variational data generative model for intrusion detection Manuel Lopez-Martin, Belen Carro , Antonio Sanchez-Esguevillas Dpto. TSyCeIT, ETSIT, Universidad de Valladolid, Paseo de Belén 15, Valladolid 47011, Spain ; mlopezm@acm.org ; belcar@tel.uva.es ; antoniojavier.sanchez@uva.es; The authors declare that there is no conflict of interest regarding the publication of this paper Abstract A Network Intrusion Detection System (NIDS) is a system which detects intrusive, malicious activities or policy violations in a host or hosts network. It is very important for any detection system the ability to access balanced and diversified data to train the system. Intrusion data rarely have these characteristics, since samples of network traffic are strongly biased to normal traffic, being difficult to access traffic associated with intrusion events. Therefore, it is important to have a method to synthesize intrusion data with a probabilistic and behavioral structure similar to the original one. In this work we provide such a method. Intrusion data have continuous and categorical features, with a strongly unbalanced distribution of intrusion labels. That is the reason why we generate synthetic samples conditioned to the distribution of labels. That is, from a particular set of labels, we generate training samples associated with that set of labels, replicating the probabilistic structure of the original data that comes from those labels. We use a generative model based on a customized Variational Autoencoder (VAE), using the labels of the intrusion class as an additional input to the network. This modification provides an advantage, as we can readily generate new data using only the labels, without having to rely on training samples as canonical representatives for each label, which makes the generation process more reliable, less complex and faster. We show that the synthetic data are similar to the real data, and that the new synthesized data can be used to improve the performance scores of common machine learning classifiers. Keywords: Intrusion detection system; Variational methods; Generative model; Neural Networks. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 161PDF 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 |