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Multi-Layer Reconstruction Errors Autoencoding and density estimate for Network Anomaly Detection

Computer Modeling in Engineering & Sciences 2021年第7期128卷 381-397页

作者： Ruikun Li Yun Li Wen He Lirong Chen Jianchao Luo University of Electronic Science and Technology of China Chengdu611730China Guangdong Weichen Information Technology Co. Ltd.Guangzhou510000China Chongqing Changan Automobile Corporation Chongqing400000China

Anomaly detection is an important method for intrusion *** recent years,unsupervised methods have been widely researched because they do not require *** example,a nonlinear autoencoder can use reconstruction errors to attain the discrimination *** method is not effective when the model complexity is high or the data contains *** method for detecting the density of compressed features in a hidden layer can be used to reduce the influence of noise on the selection of the threshold because the density of abnormal data in hidden layers is smaller than normal ***,compressed features may lose some of the high-dimensional distribution information of the original *** this paper,we present an efficient anomaly detection framework for unsupervised anomaly detection,which includes network data capturing,processing,feature extraction,and anomaly *** employ a deep autoencoder to obtain compressed features and multi-layer reconstruction errors,and feeds them the same to the Gaussian mixture model to estimate the *** proposed approach is trained and tested on multiple current intrusion detection datasets and real network scenes,and performance indicators,namely accuracy,recall,and F1-score,are better than other autoencoder models.

关键词： Anomaly detection deep autoencoder density estimate

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RATES OF STRONG UNIFORM CONVERGENCE OF NEAREST NEIGHBOR density estimateS ON ANY COMPACT SET

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Applied Mathematics and Mechanics(English Edition) 1990年第4期11卷 385-393页

作者：张涤新 Guizhou Planning College Guiyang

In this paper, we propose the concept of rates of strong uniform convergence of nearest neighbor density estimates on any compact set and obtain some better convergence rates. Hence the problem of the strong uniform c... 详细信息

关键词： Mathematical Modeling Convergence Rate Industrial Mathematic density estimate Uniform Convergence

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African Lion Population estimates in Tanzania’s Ruaha National Park

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Open Journal of Ecology 2022年第8期12卷 558-569页

作者： Michael H. Kimaro Hillary T. Mrosso Simon J. Chidodo Nyemo A. Chilagane Fenrick F. Msigwa George B. Bulenga Rose P. Kicheleri Charles P. Mgeni Rajabu J. Kangile Elisante A. Kimambo Courtney Hughes Camille Warbington Helen Mchaki Daniel Mathayo Halima R. Kiwango GELIFES Institute University of Groningen Groningen Netherlands Tanzania Research and Conservation Organization Morogoro Tanzania CFWT College Sokoine University of Agriculture Morogoro Tanzania SAEBS College Sokoine University of Agriculture Morogoro Tanzania University of Alberta Alberta Canada Conservation Science Department Ruaha National Park Iringa Tanzania

Tanzania is considered a country with the largest number of African lions (Panthera leo). However, the continued absence of ecological population estimates and understanding of the associated factors influencing lion distribution hinders the development of conservation planning. This is particularly true in the Ruaha-Rungwa landscape, where it was estimated that more than 10% of the global lion population currently resides. By using a call-back survey method, we aimed to provide population estimates (population size and density) of African lions in the Ruaha National Park, between wet (March 2019) and dry (October 2019) seasons. We also assessed the key factors that influenced the distribution of the observed lions towards call-back stations. Ferreira & Funston’s (2010) formula was used to calculate population size and in turn used to estimate density in the sampled area, while the Generalized Linear Model (GLMM) with zero-inflated Poisson error distribution was used to determine factors that influence the distribution of the observed lions to call-back stations. The population size we calculated for the sampled area of 3137.2 km2 revealed 286 lions (95% CI, 236 - 335) during the wet season, and 196 lions (95% CI, 192 - 200) during the dry season. The density of lions was 9.1/100 km2 during the wet season, and 6.3/100 km2 during the dry season. Distance to water source had a significant negative effect on the distribution of the observed lions to the call-back stations, while habitat had a marginal effect. Our findings show that, although lion population estimates were larger during the wet season than the dry season, the season had no effect on the distribution of the observed lions to call-back stations. We suggest that the proximity to water sources is important in study design. Further, we suggest that density and population size are useful indices in identifying conservation area priorities and lion coexistence strategies.

关键词： Population Size density estimate Call-Back Survey African Lion Conservation

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The Difference between Consecutive Primes

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Acta Mathematica Sinica,English Series 1985年第2期1卷 109-118页

作者：陆鸣皋 Department of Mathematics University of Science and Technology of China

1 .Introduetion This paPer disousses two Problem which relate toconseoutive Primes. Let。be the th Prime,and d。+1We want to find a function

关键词： Positive Integer Positive Constant Natural Number density estimate Normal density

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