dfang

set http or socks proxy environment variables

# set http proxy
export http_proxy=http://PROXYHOST:PROXYPORT

# set http proxy with user and password
export http_proxy=http://USERNAME:PASSWORD@PROXYHOST:PROXYPORT

# set http proxy with user and password (with special characters)

dfang

Here are some examples on how to use Telegram bot api via CURL

Prerequisites

For getting messages in private chat with bot

• Create a bot using @BotFather, get it's token
• Start conversation with bot
• Run following curl command

curl https://api.telegram.org/bot/getUpdates | grep -Po '"from":{"id":.+?,'

dfang

#!/bin/sh

if [[ `id -u` != 0 ]]; then
    echo "Must be root to run script"
    exit
fi
 
read -p "Enter user name and press [ENTER]: " UserName

if [[ $UserName == `dscl . -list /Users UniqueID | awk '{print $1}' | grep -w $UserName` ]]; then

dfang

为什么流量可以进行分类

这里的“流量”一般定义为中间人观测到的一组由（时间，方向，包大小）元数据组成的序列 [Cai2014]。其源头是应用层的读写操作，经过传输层协议的变换（分片、协议状态机、加密等），流量序列产生一定变化。但是这种变化非常有限，因为流量的发生过程本质是确定性的，随机因素较小，因此对于特定环境中的特定应用（浏览器访问 google.com）各种流量特征体现出相当大的一致性和独特性，这就使“从流量特征识别应用”的监督学习问题成为可能。虽然有若干不利因素使得确定性下降，例如多层次上软件多版本的排列组合爆炸、有状态的缓存、流水线和连接复用、用户随机行为，但是因为版本的幂律分布、应用层读写操作间的依赖关系、流量特征和检测算法的改进等原因，分类依然具有相当的可行性。

分类的对象：流量应用分类与网站指纹攻击

根据分类的对象产生了两个相近但是不同的研究领域。从流量特征中分类应用类型的被称为流量分类（traffic classification），从流量特征中分类所访问网站或者网页的被称为网站指纹（website fingerprinting）。以机器学习的方法而论前者是比后者更弱但本质相同的一个问题。

这两类攻击的威胁类型不同。流量分类威胁的是可用性，如果GFW检出流量是隧道应用然后进行封锁，则破坏了可用性。而网站指纹威胁的是匿名性和隐私，如果从隐秘流量中检出是谁在访问哪个网站，则破坏了匿名性，丝绸之路就是这样被FBI破获的。

dfang

调查目的：了解当前各基于TLS的协议方案中ClientHello的指纹独特性。理论背景见 https://arxiv.org/abs/1607.01639 。

指纹数据库：

• https://tlsfingerprint.io
• https://github.com/cisco/mercury

naiveproxy v78.0.3904.70-4

（利益相关：我是这个的作者）

dfang

services:
    - docker:dind

stages:
    - development
    - production

variables:
    DOCKER_DRIVER: overlay2
    DOCKER_TLS_CERTDIR: ''

dfang

#!/bin/bash

#Harbor on Ubuntu 18.04

#Prompt for the user to ask if the install should use the IP Address or Fully Qualified Domain Name of the Harbor Server
PS3='Would you like to install Harbor based on IP or FQDN? '
select option in IP FQDN
do
    case $option in
        IP)

dfang

Setup keyless login

1. On local make an RSA key if you haven't already (ssh-keygen)
2. Copy that key into .ssh/authorized_keys on the first (i.e. bastion) server (ssh-copy-id user@bastion.server) 2b. Test that you can ssh user@bastion.server and connect without password
3. ssh to bastion.server and create an RSA key on bastion.server if you haven't already (ssh-keygen)
4. ssh-copy-id to the server you want to access (what I am calling real.server) 4b. test that you can ssh from bastion.server to real.server without entering a password

To copy from server to local

rsync -e "ssh user@bastion.server ssh" user@real.server:/path/to/file /path/to/local/dest

dfang

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

dfang

--- PSQL queries which also duplicated from https://github.com/anvk/AwesomePSQLList/blob/master/README.md
--- some of them taken from https://www.slideshare.net/alexeylesovsky/deep-dive-into-postgresql-statistics-54594192

-- I'm not an expert in PSQL. Just a developer who is trying to accumulate useful stat queries which could potentially explain problems in your Postgres DB.

------------
-- Basics --
------------

-- Get indexes of tables