A Study of Deep Learning for Network Traffic Data Forecasting

We present a study of deep learning applied to the domain of network traffic data forecasting. This is a very important ingredient for network traffic engineering, e.g., intelligent routing, which can optimize network performance, especially in large networks. In a nutshell, we wish to predict, in advance, the bit rate for a transmission, based on low-dimensional connection metadata ("flows") that is available whenever a communication is initiated. Our study has several genuinely new points: First, it is performed on a large dataset (~50 million flows), which requires a new training scheme that operates on successive blocks of data since the whole dataset is too large for in-memory processing. Additionally, we are the first to propose and perform a more fine-grained prediction that distinguishes between low, medium and high bit rates instead of just "mice" and "elephant" flows. Lastly, we apply state-of-the-art visualization and clustering techniques to flow data and show that visualizations are insightful despite the heterogeneous and non-metric nature of the data. We developed a processing pipeline to handle the highly non-trivial acquisition process and allow for proper data preprocessing to be able to apply DNNs to network traffic data. We conduct DNN hyper-parameter optimization as well as feature selection experiments, which clearly show that fine-grained network traffic forecasting is feasible, and that domain-dependent data enrichment and augmentation strategies can improve results. An outlook about the fundamental challenges presented by network traffic analysis (high data throughput, unbalanced and dynamic classes, changing statistics, outlier detection) concludes the article.Comment: 16 pages, 12 figures, 28th International Conference on Artificial Neural Networks (ICANN 2019

A set of novel multiplex Taqman real-time PCRs for the detection of diarrhoeagenic Escherichia coli and its use in determining the prevalence of EPEC and EAEC in a university hospital

<p>Abstract</p> <p>Background</p> <p>Accurate measurement of the incidence of diarrhoeagenic <it>E. coli </it>in patients with diarrhoea is hindered by the current methods of detection and varies from country to country. In order to improve the diagnosis of diarrhoeagenic <it>E. coli </it>(DEC), we developed a set of multiplex TaqMan real-time PCRs designed to detect the respective pathogens from an overnight stool culture.</p> <p>Methods</p> <p>Over the period Jan. 2006 to Dec. 2006 all stool specimens (n = 1981) received were investigated for EPEC and EAEC.</p> <p>Results</p> <p>Of these, 371 specimens had no growth of <it>Enterobacteriaceae</it>. Of the remaining 1610 specimens 144 (8,9%) were positive for EPEC and 78 (4,8%) positive for EAEC. Among the EPEC positive stool specimens 28 (19,4%) were received from the tropical diseases unit, 49 (34%) from the paediatric dept. and 67 (46,5%) from the remainder of the wards. The EAEC were distributed as follows: 39 (50%) - tropical diseases, 19 (24,4%) -paediatrics and 20 (25,6%) other wards. Proportionately more EAEC and EPEC were found in children less than 3 years of age than other age groups. In only 22,2% of the detected EPEC and 23% of EAEC was the investigation requested by hospital staff.</p> <p>Conclusions</p> <p>This is, to our knowledge, the first study using a multiplex TaqMan PCR for the successful detection of diarrhoeagenic <it>E. coli</it>. In conclusion, due to the high prevalence of DEC detected, investigation of EPEC and EAEC should be recommended as a routine diagnostic test for patients with infectious diarrhoea.</p