CentOS7配置VSFTP服务器

[1] 安装VSFTP

[root@localhost ~]# yum -y install vsftpd

[2] 配置vsftpd.conf文件

[root@localhost ~]# vi /etc/vsftpd/vsftpd.conf

# Example config file /etc/vsftpd/vsftpd.conf 
# 
# The default compiled in settings are fairly paranoid. This sample file 
# loosens things up a bit, to make the ftp daemon more usable. 
# Please see vsftpd.conf.5 for all compiled in defaults. 
# 
# READ THIS: This example file is NOT an exhaustive list of vsftpd options. 
# Please read the vsftpd.conf.5 manual page to get a full idea of vsftpd's 
# capabilities. 
# 
# Allow anonymous FTP? (Beware - allowed by default if you comment this out). 
#anonymous_enable=YES 
# 
# Uncomment this to allow local users to log in. 
local_enable=YES 
# 
# Uncomment this to enable any form of FTP write command. 
write_enable=YES 
# 
# Default umask for local users is 077. You may wish to change this to 022, 
# if your users expect that (022 is used by most other ftpd's) 
local_umask=022 
# 
# Uncomment this to allow the anonymous FTP user to upload files. This only 
# has an effect if the above global write enable is activated. Also, you will 
# obviously need to create a directory writable by the FTP user. 
#anon_upload_enable=YES 
# 
# Uncomment this if you want the anonymous FTP user to be able to create 
# new directories. 
#anon_mkdir_write_enable=YES 
# 
# Activate directory messages - messages given to remote users when they 
# go into a certain directory. 
dirmessage_enable=YES 
# 
# The target log file can be vsftpd_log_file or xferlog_file. 
# This depends on setting xferlog_std_format parameter 
xferlog_enable=YES 
# 
# Make sure PORT transfer connections originate from port 20 (ftp-data). 
connect_from_port_20=YES 
# 
# If you want, you can arrange for uploaded anonymous files to be owned by 
# a different user. Note! Using "root" for uploaded files is not 
# recommended! 
#chown_uploads=YES

#chown_username=whoever 
# 
# The name of log file when xferlog_enable=YES and xferlog_std_format=YES 
# WARNING - changing this filename affects /etc/logrotate.d/vsftpd.log 
#xferlog_file=/var/log/xferlog 
# 
# Switches between logging into vsftpd_log_file and xferlog_file files. 
# NO writes to vsftpd_log_file, YES to xferlog_file 
xferlog_std_format=YES 
# 
# You may change the default value for timing out an idle session. 
idle_session_timeout=600 
# 
# You may change the default value for timing out a data connection. 
data_connection_timeout=120 
# 
# It is recommended that you define on your system a unique user which the 
# ftp server can use as a totally isolated and unprivileged user. 
#nopriv_user=ftpsecure 
# 
# Enable this and the server will recognise asynchronous ABOR requests. Not 
# recommended for security (the code is non-trivial). Not enabling it, 
# however, may confuse older FTP clients. 
#async_abor_enable=YES 
# 
# By default the server will pretend to allow ASCII mode but in fact ignore 
# the request. Turn on the below options to have the server actually do ASCII 
# mangling on files when in ASCII mode. 
# Beware that on some FTP servers, ASCII support allows a denial of service 
# attack (DoS) via the command "SIZE /big/file" in ASCII mode. vsftpd 
# predicted this attack and has always been safe, reporting the size of the 
# raw file. 
# ASCII mangling is a horrible feature of the protocol. 
ascii_upload_enable=YES 
ascii_download_enable=YES 

# 
# You may fully customise the login banner string: 
ftpd_banner=Welcome to lightnear FTP service. 
# 
# You may specify a file of disallowed anonymous e-mail addresses. Apparently 
# useful for combatting certain DoS attacks. 
#deny_email_enable=YES 
# (default follows) 
#banned_email_file=/etc/vsftpd/banned_emails 
# 
# You may specify an explicit list of local users to chroot() to their home 
# directory. If chroot_local_user is YES, then this list becomes a list of 
# users to NOT chroot(). 
chroot_local_user=YES 
#chroot_list_enable=YES 
# (default follows) 
#chroot_list_file=/etc/vsftpd/chroot_list 
# 
# You may activate the "-R" option to the builtin ls. This is disabled by 
# default to avoid remote users being able to cause excessive I/O on large 
# sites. However, some broken FTP clients such as "ncftp" and "mirror" assume 
# the presence of the "-R" option, so there is a strong case for enabling it. 
ls_recurse_enable=YES 
# 
# When "listen" directive is enabled, vsftpd runs in standalone mode and 
# listens on IPv4 sockets. This directive cannot be used in conjunction 
# with the listen_ipv6 directive. 
listen=YES 
# 
# This directive enables listening on IPv6 sockets. To listen on IPv4 and IPv6 
# sockets, you must run two copies of vsftpd with two configuration files. 
# Make sure, that one of the listen options is commented !! 
#listen_ipv6=YES

pam_service_name=vsftpd 
userlist_enable=YES 
userlist_deny=NO 
local_root=/var/www/
tcp_wrappers=YES

use_localtime=YES

[3] 增加FTP帐户,设置账户密码

[root@localhost ~]# useradd ftpuser -s /sbin/nologin
[root@localhost ~]# passwd ftpuser

[4] 编辑user_list文件,允许ftpuser用户访问FTP

[root@localhost ~]# vi /etc/vsftpd/user_list
# vsftpd userlist
# If userlist_deny=NO, only allow users in this file
# If userlist_deny=YES (default), never allow users in this file, and
# do not even prompt for a password.
# Note that the default vsftpd pam config also checks /etc/vsftpd/ftpusers
# for users that are denied.
root
bin
daemon
adm
lp
sync
shutdown
halt
mail
news
uucp
operator
games
nobody
ftpuser

[5] 建立访问的根目录,并设置访问权限

[root@localhost ~]# mkdir /var/www
[root@localhost ~]# chown -R ftpuser /var/www
[root@localhost ~]# chmod -R 755 /var/www

[6] 开启vsftpd服务

[root@localhost ~]# service vsftpd start
Starting vsftpd for vsftpd:                                [  OK  ]

[7] 默认开启vsftp服务

[root@localhost var]# chkconfig vsftpd on

 

另外要设置根目录的权限,以免无法进行上传写入等操作。

 

 

注意:

在使用设置好的FTP账号连接时可能会报错

500 OOPS: vsftpd: refusing to run with writable root inside chroot ()

从2.3.5之后,vsftpd增强了安全检查,如果用户被限定在了其主目录下,则该用户的主目录不能再具有写权限了!如果检查发现还有写权限,就会报该错误。

vsftpd的配置文件中增加:

allow_writeable_chroot=YES 

 

转载于:https://www.cnblogs.com/suiyuewuxin/p/7207565.html

从零构建工业级SPI-CAN网关:CH347与CH9431的软硬件协同设计实战 本文详细介绍了基于CH347和CH9431芯片构建工业级SPI-CAN网关的完整方案。通过硬件架构设计、Linux驱动定制、并发处理优化和EMC抗干扰测试,实现高可靠性通信网关,适用于工业自动化和汽车电子等严苛环境。 阅读详情

相关推荐

FPGA时序分析中的工艺角:从PVT原理到建立保持时间验证

在数字电路设计中,静态时序分析(STA)是确保芯片功能正确和性能达标的核心验证手段。其基本原理是通过计算信号在逻辑路径上的传播延迟,检查建立时间和保持时间是否满足寄存器采样要求。这项技术的核心价值在于,它能在芯片制造前就预测并消除时序违规,极大提高设计成功率。然而,实际芯片性能会受到工艺波动、电压变化和温度漂移(合称PVT)的显著影响。为了覆盖这些不确定性,时序分析必须采用基于工艺角的边界模型——用最慢的工艺角模型验证建立时间,用最快的工艺角模型验证保持时间。这种分析方法广泛应用于FPGA和ASIC设计流程

weixin_30181209的博客 199

vsftp无法使用主动模式故障解决

部分思路转自https://www.cnblogs.com/linn/p/4169986.html这时候可以看到机器的真正IP。我们需要设置pasv_address=本机ip【就是我们能访问的外网IP】 pasv_addr_resolve=yes防火墙开启端口21,20等需要的端口。阿里云强制升级ecs服务器另我崩溃的24小时。...

shuishen49的专栏 2750

SAGA GIS英文版进行地形分析,计算常用的地形因子等

SAGA GIS中文版是一款专业的自动化地球科学分析系统软件。SAGA GIS最新版界面简洁,操作简便,用户使用时输入要分割的影像,选择要计算特征的影像或者波段,设置越大,得到的斑块越大;反之,则越小,点击Okay,得到分割影像图。SAGA GIS中文版主要通过选择适合你影像的格式,加载影像。 SAGA GIS全称是System for Automated Geoscientific Analyses,是一种用于编辑空间数据的地理信息系统(GIS)计算机程序,它是免费的开源软件,SAGA GIS旨在为科学家

qq_35794085的博客 5645

centos后台登录ftp_CentOS7 下安装和配置本地用户登录VSFTP服务器

CentOS7 下安装和配置本地用户登录VSFTP服务器一、安装VSFTP# yum -y install vsftpd注意提升权限:#su root二、配置vsftpd.conf 文件[root@localhost ~]# vi /etc/vsftpd/vsftpd.conf# Example config file /etc/vsftpd/vsftpd.conf## The default c...

weixin_39744316的博客 516

相机通用类之海康相机,软触发硬触发(飞拍),并输出halcon格式对象

如何显示图像:可以访问我文章开头的博客,通过编写相机通用类即可,程序中是有继承通用类,通过访问相机通用的获取图像数据,从而传输图像数据。具体可以查看相机通用类。整体过程其实比较简单,由于海康相机没有像一些品牌相机一样可以直接使用序列号开启相机,所以我们需要通过对相机信息进行索引,根据索引到的相机序列号来开启相机。

m0_51559565的博客 7621

更换光猫的原理与方法

当你给装维打电话说你需要更换光猫时,对于装维来说,他并不需要知道怎么更换,他只需要生成一张工单,由后台进行业务操作后,在去用户家把新光猫给装好即可,此时装维到底起到了什么作用呢?我想可能只是运输员吧,把一个设备从一个地方运到另一个地方罢了,此时并不能指望装维做什么,因为本质上来说装维的能力真的很差很差,我是不知道那些装维哪来的勇气做视频,还说自己什么什么能力很强?很强吗?吹牛的技术确实很强。 更换光猫之前,需要先明白为什么要更换光猫,怎么更换光猫,更换后不能用怎么办?怎么才能更换成功,换光猫以后被限速了怎么

qq_34146290的博客 1万+

CentOS7 下安装和配置本地用户登录VSFTP服务器

一、安装VSFTP # yum -y install vsftpd注意提升权限: #su root 二、配置vsftpd.conf 文件 [root@localhost ~]# vi /etc/vsftpd/vsftpd.conf # Example config file /etc/vsftpd/vsftpd.conf # # The default compi

u010379324的博客 1万+

使用光流进行 Warp 的流程

光流描述了图像帧中每个像素的运动。前向光流:描述了帧中每个像素在帧中的移动位置。后向光流:描述了帧中每个像素在帧​ 中的移动位置。水平方向的位移(x 方向),即每个像素水平移动了多少像素。垂直方向的位移(y 方向),即每个像素垂直移动了多少像素。使用光流进行 Warp 的流程Warp 是通过光流对图像进行变换的操作,旨在将前一帧的像素根据光流场映射到下一帧的对应位置。Step 1: 计算光流我们首先需要通过光流网络(如 RAFT)来估算两帧之间的光流。我们可以获得前向光流(从帧​ 到帧。

qq_44199605的博客 2108

CentOS 7.0安装配置Vsftp服务器

一、配置防火墙,开启FTP服务器需要的端口 CentOS 7.0默认使用的是firewall作为防火墙,这里改为iptables防火墙。 1、关闭firewall: systemctl stop firewalld.service #停止firewall systemctl disable firewalld.service #禁止firewall开机启动 2、安装iptables防火墙

CatEatApple的专栏 536

SCADA|RESTful学习,Apipost通过GET获取KingSCADA实时数据

本文介绍了如何使用RESTful实现SCADA与MES系统对接。主要包括:1)对比RESTful与其他协议的性能指标;2)KingSCADA作为Server/Client的三种应用场景;3)详细演示KingSCADA3.8作为Server端的配置过程,包括端口设置和API测试。通过Apipost工具测试获取变量值及数据回写接口,其中数据回写需使用MD5加密密码。文章最后指出回写测试失败的情况,但未分析具体原因。全文以实际工程应用为导向,提供了具体操作步骤和技术细节。

做有价值的事,积累有价值的笔记! 626

从 Chat 提示词到可运行 Fiori 应用,ADT for VS Code 正在把 RAP 开发带进 Agent 时代

本文探讨了在VS Code中使用AI辅助进行ABAP RAP开发的新模式。文章指出,传统RAP开发需要手工创建数据库表、CDS视图、行为定义等十多个关联对象,命名和关联关系容易出错。现在通过GitHub Copilot Agent Mode配合ADT MCP Server,开发者可用自然语言描述业务场景直接生成完整的RAP业务对象。这种生成式开发的核心价值在于:1)通过标准化工具接口(MCP)连接ABAP后端,确保生成符合规范的RAP对象;2)能正确处理多实体组合场景下的命名一致性和关联关系;3)保持生成结

2007 年 ~ 2025 年,深耕 SAP 技术 18 年 89

CentOS 7.0安装配置Vsftp服务器步骤详解

安装Vsftp讲过最多的就是在centos6.x版本中了,这里小编看到有朋友写了一篇非常不错的CentOS 7.0安装配置Vsftp服务器教程,下面整理分享给各位。 一、配置防火墙,开启FTP服务器需要的端口 CentOS 7.0默认使用的是firewall作为防火墙,这里改为iptables防火墙。 1、关闭firewall: systemctl stop firewalld.servi...

weixin_34255055的博客 174

71.qt quick-可伸展菜单-抽屉栏示例 通用QML界面(一键换肤)

特点: 支持动态添加item、删除item、自适应界面 附带添加删除、展开等动画 同时支持qwidget、qml加载 支持number提示(比如消息数量等) 侧边抽屉栏支持动画伸展 支持换肤、拖拽、最大化等

抵抗时间扭曲,坚持做一件有意义的事 5286

linux7创建vsftp服务器,Centos 7 FTP(vsftp)服务安装及配置

作为前端工程师,要想在中国有一席之地,你不得不作出不可能的努力,把自己变成一个全能的人。你不仅仅要会 HTML、CSS、JavaScript、PhotoShop、Node.js、Angular.js、以及各种前端流行框架如Boostrap、Kube、UIKIT、amazeui 等等,你还得会配置服务器Linux、Windows。所以下面我们变来聊聊的服务器的配置,在本文中我们要介绍的是在 Cen...

weixin_36179469的博客 329

OV9734 DATASHEET,完整规格书

OV9734完整规格书,包括电气参数,尺寸规格,寄存器配置表等

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

linux centos 7中配置vsftp服务器简单配置

注:首先需要先执行“yum install vsftpd -y”进行vsftp安装,如报错,尝试在联外网环境下进行安装1)添加FTP用户名和密码,此次以"NAStest/ceshinas"为例,添加后FTP根目录默认为"/home/NAStest"useradd NAStestpasswd NAStest   //执行后会两次提示输入密码,输入“ceshinas”即可2)给FTP根目录添加读写权限...

琴怀诗韵 464

锂电池充电器电路设计 里面有电路

锂电池充电器电路设计 里面有电路 3.7V升压到5V电路设计。

CentOS 7.0安装配置vsftp服务器

一、配置防火墙,开启FTP服务器需要的端口 CentOS 7.0默认使用的是firewall作为防火墙,这里改为iptables防火墙。 1、关闭firewall: systemctl stop firewalld.service #停止firewall systemctl disable firewalld.service #禁止firewall开机启动 2、安装iptables防火墙 yum ...

ws1039的博客 207

win10 跳过U8环境检测的IE WebControls.rar

win10 安装u8的时候 提示IE WebControls 不通过,这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后,重新检测通过。

​Linux centos 7 安装vsftp-server服务器

Linuxcentos7安装vsftp-server服务器 一、系统环境 #cat/etc/redhat-release CentOSLinuxrelease7.6.1810(Core) 二、安装vsftp服务 #yum-yinstallvsftpddb4*(db4配置虚拟用户生成db库文件) 三、配置vsftp服务器主要配...

weixin_34387468的博客 159

Centos7_vsftp服务器全局配置

4、在/etc/vsftpd目录下创建虚拟ftp认证的用户账户文件并转换为数据库文件。Centos7_IP地址:192.168.10.1。6、创建ftp根目录及虚拟用户映射的系统用户。(文件奇数行写用户名,偶数行写账户密码)5、创建支持虚拟用户的PAM认证文件。2、安装vsftpd、ftp服务。3、配置vsftp文件。

简单安全 352

CentOS7下配置Linux vsftp服务器

首先下载vsftp首先下载vsftphttp://pkgs.org/download/vsftpd这里下载的版本为:vsftpd-2.2.2-12.el6_5.1.x86_64.rpmPS: 如果你有源,你也可以直接yum install vsftpd -&gt; yum install ftp1.查看是否安装vsftp rpm -qa | grep vsftpd 如果出现vsftpd-2.0.5...

輚至消亡 427

CentOS7搭建vsftp服务器

今天简单配置匿名访问及用户验证的相关配置,基于虚拟用户的配置敬请期待下一篇 搭建vsftp服务器首先要确保虚拟机的防火墙和seLinux防火墙都是关闭的 一、ftp文件传输服务 1.ftp连接的端口号 21:传输控制使用,进行身份验证授权访问 20:传输数据使用 2.ftp连接模式 主动模式:服务器端通过20号端口给客户端传输数据,POST指令表示主动连接,默认工作在主动模式 被动模式:服务器端开...

:Struggle. 558

CentOS7安装配置VSFTP

CentOS7安装配置VSFTP 搭建ftp服务器,设置不允许匿名用户登录,只允许本地用户登录,且将活动范围限制在其家目录 **服务器端:** 安装vsftpd yum install -y vsftpd 启动ftp服务 systemctl start vsftpd 修改配置文件 vim /etc/vsftpd/vsftpd.conf 不允许匿名用户登录 anonymous_enable=NO 只允许本地用户登录 local_enable=YES 将活动范围限制在其家目录 chroot_local_us

jjxws的博客 404

CentOS 7.2安装配置Vsftp服务器

一、配置防火墙,开启FTP服务器需要的端口 CentOS 7.2默认使用的是firewall作为防火墙,这里改为iptables防火墙。 1、关闭firewall: systemctl stop firewalld.service #停止firewall systemctl disable firewalld.service #禁止firewall开机启动 2、安装iptables防火墙...

weixin_30692143的博客 97

CentOS 7 vsftpd服务器搭建记录——筑梦之路

centos 7 vsftp 服务器搭建记录 yum install vsftpd -y 配置文件示例 anonymous_enable=NO local_enable=YES write_enable=YES local_umask=022 dirmessage_enable=YES xferlog_enable=YES connect_from_port_20=YES xferlog_std_format=YES idle_session_timeout=600 ascii_upload_en.

筑梦之路 285
上一篇: 在CentOS6,CentOS7安装 Let'sEncrypt 免费SSL安全证书
下一篇: 利用nginx与nginx-rtmp-module搭建流媒体服务器实现直播
aaa2008159
博客等级 码龄12年 0粉丝 0原创
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符  | 博主筛选后可见
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值