Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

Cloud Computing(CC)is the preference of all information technology(IT)organizations as it offers pay-per-use based and flexible services to its users.But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders.Intrusion Detection System(IDS)refers to one of the commonly utilized system for detecting attacks on cloud.IDS proves to be an effective and promising technique,that identifies malicious activities and known threats by observing traffic data in computers,and warnings are given when such threatswere identified.The current mainstream IDS are assisted with machine learning(ML)but have issues of low detection rates and demanded wide feature engineering.This article devises an Enhanced Coyote Optimization with Deep Learning based Intrusion Detection System for Cloud Security(ECODL-IDSCS)model.The ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic(ADASYN)technique.For detecting and classification of intrusions,long short term memory(LSTM)model is exploited.In addition,ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud environment.Once the presented ECODL-IDSCS model is tested on benchmark dataset,the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.

Coyote Predation Effects on White-Tailed Deer Fawns

Coyotes (Canis latrans) are a relatively new predator to the southeastern United States, and may be negatively impacting white-tailed deer (Odocoileus virginianus;hereafter, deer) populations. Our objectives were to evaluate the impacts of coyotes on deer fawns by assessing deer fawn survival and cause-specific mortality, and gain an understanding of factors affecting fawn survival and coyote predation. We captured and radio collared 30 fawns in the Red Hills region of Florida and Georgia, USA (2012-2013). Fawns were monitored for 12 weeks for survival and cause-specific mortality, and we quantified habitat and environmental characteristics of birth sites. Predation (n = 19;95%) was the leading cause of fawn mortality (n = 20;67%), with coyote predation (n = 14;74%) being the most important type of predation. Survival rates for all fawns were greater (P = 0.048) where coyotes were removed compared to non-removal sites, with 50% and 25% of fawns surviving to 12 weeks on coyote-removal and non-removal sites, respectively. Survival rates of fawns ultimately predated by coyotes were greater (P = 0.096) on coyote-removal than non-removal sites, with 40% and 50% of fawns predated by coyotes within 12 weeks on coyote-removal and non-removal sites, respectively. Survival of all fawns and those predated by coyotes was lower when fawns were born at sites with greater hardwood basal area, total basal area, and canopy closure;and survival improved if born in or near hardwood, natural pine, and managed (planted) pine cover types. Increased canopy cover within 10 m of the birth site was selected by adult females for birth sites of all fawns and those that were predated by coyotes. Compared with fawns that lived, all dying fawns and those predated by coyotes had less shrub cover within 5 m and less grass cover at and within 10 m of the birth site. Coyote removal increased fawn daily survival rates, and habitat played a role in fawn survival.

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

In order to address the problems of Coyote Optimization Algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid Coyote Optimization Algorithm(here-inafter referred to as FHCOA)based on chaotic initialization and reverse learning strategy is proposed,and its effect on image thresholding is verified.Through chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic sequence.Such sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization algorithm.Therefore,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard COA.By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then formed.In the process of algorithm traversal,the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively,which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence.Based on the above improvements,the coyote optimization algorithm has better global convergence and computational robustness.The simulation results show that the algorithmhas better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

Factors Determining Coyote (<i>Canis latrans</i>) Diets

Although studies have documented the potential for coyote (Canis latrans) food use to negatively affect wildlife populations and domesticated animals, they are often equivocal, possibly because most are of small spatial extent, and little is known of factors determining coyote diets. Our objectives were to quantify the diet and identify factors determining coyote food use, particularly game species and livestock, over a large spatial and temporal extent. Contents of gastrointestinal tracts were identified from 263 coyotes opportunistically obtained from hunters, trappers, and as road-kills throughout Florida, 2011-2015. We employed logistic regression in an information-theoretic framework to understand determinants of coyote food use. Coyotes were opportunistic and omnivorous foragers with a diverse diet of vegetation, insects, birds, reptiles, amphibians, and more than 25 species of mammals (including important game species and livestock). They commonly consumed 11 food items (Virginia opossum [Didelphis virginiana], non-mast vegetation, feral hog [Sus scrofa], northern raccoon [Procyon lotor], insects, rabbits (Sylvilagus spp.), skunks [Mephitis mephitis and Spilogale putorius], white-tailed deer (Odocoileus virginianus), mast, birds, and cows [Bos taurus]). Food use was determined by coyote age, sex, and body mass, season of the year, deer hunting and fawning seasons, livestock calving season, and coyote collection method and location/region. As coyotes expand their range and numbers, conservationists may find it useful to understand how this opportunistic and adaptable predator uses available food sources to reduce conflict across the landscape.

Winter Diet in Correlation to Sexual Dimorphism in Pennsylvania Coyotes (<i>Canis latrans</i>)

Coyotes (Canis latrans) have been rapidly expanding into the Northeastern Region of the United States since the mid 1900’s most likely due to anthropogenic changes in their habitat. Several studies suggest that in addition to being top predators, coyotes are opportunistic feeders and are able to switch prey based on availability and density. Their generalist predation approach allows variation in their diets, and a widespread impact on the ecosystems in which they reside. In this paper, one hundred and seventy seven coyote stomachs were obtained throughout Pennsylvania from 2009-2012 and were dissected to define coyote winter diet. The contents were identified using ad hoc reference bone collections and a set of SEM hair images. Stomach contents were used to identify any correlation between sexual dimorphism and winter diets. It is hypothesized that if Pennsylvania’s coyotes show sexual dimorphism, male diets may differ from females. Being able to hunt larger animals may provide males the benefit of a higher caloric return. As a by-product of this research we are detailing a list of prey items found in the winter diet of C. latrans arranged by sex and location.

Coyote Linux在学校机房中的使用

根据学校机房中遇到的网络问题,从方便、简单、经济、实用的角度提出用CoyoteLinux软路由防火墙进行解决,并就使用中的一些问题进行了介绍。

Coyote Linux在学校机房中的使用

根据学校机房中遇到的网络问题,从方便、简单、经济、实用的角度提出用Coyote Linux软路由防火墙进行解决,并就使用中的一些问题进行了介绍。

Coyote Linux中实现流一量控制

该文主要介绍 Linux 网络流量控制基本原理和路由软件 Coyote Linux,并联系实际网络环境,使用 Coyote Linux 配置路由服务器,利用 TC 命令进行不同网络流量需求的控制,实现了专业路由器的流量控制功能。

丛林狼超声速掠海飞行靶弹

首先简单介绍了丛林狼GQM-163A超声速掠海飞行靶弹的研制背景及其曲折的发展历程,系统介绍了靶弹结构和主要组成部件,通过综合评价,阐述了该靶弹的成功研制在冲压发动机的发展史中具有里程碑的意义。

基于Linux的虚拟局域网软路由的设计

介绍了目前的中小型网络概况、软路由的概念及Linux操作系统的特点,分析了中小型网络设计的不足,根据RIP路由协议,给出一套完整的Linux的虚拟局域网软路由解决方案,从路由器和个人电脑的对比入手,分析了应用的可行性,详细介绍了Coyote Linux的安装及管理。

Canid Social Structure and Density Dependence Improve Predator-Prey Models of <i>Canis latrans</i>and <i>Lepus californicus</i>in Curlew Valley, UT

Prominent examples of predator-prey oscillations between prey-specific predators exist, but long-term data sets showing these oscillations are uncommon. We explored various models to describe the oscillating behavior of coyote (Canis latrans) and black-tailed jackrabbits (Lepus californicus) abundances in a sagebrush-steppe community in Curlew Valley, UT over a 31-year period between 1962 and 1993. We tested both continuous and discrete models which accounted for a variety of mechanisms to discriminate the most important factors affecting the time series. Both species displayed cycles in abundance with three distinct peaks at ten-year intervals. The coupled oscillations appear greater in the mid-seventies and a permanent increase in the coyote density seems apparent. Several factors could have influenced this predator-prey system including seasonality, predator satiation, density dependence, social structure among coyotes, and a change in the coyote bounty that took place during the course of data collection. Maximum likelihood estimation was used to obtain parameter values for the models, and Akaike Information Criterion (AIC) values were used to compare models. Coyote social structure and limiting resources in the form of density-dependence and satiation seemed to be important factors affecting population dynamics.