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##TCP FLAGS##

Unskilled Attackers Pester Real Security Folks
==============================================
                     TCPDUMP FLAGS
Unskilled =  URG  =  (Not Displayed in Flag Field, Displayed elsewhere) 
Attackers =  ACK  =  (Not Displayed in Flag Field, Displayed elsewhere)
Pester    =  PSH  =  [P] (Push Data)
Real      =  RST  =  [R] (Reset Connection)
Security  =  SYN  =  [S] (Start Connection)

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/*
    An example of using raw sockets.

    You can capture packets by tcpdump:
        tcpdump -X -s0 -i lo -p udp
 */

#include <stdlib.h>
#include <unistd.h>
#include <stdio.h>

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import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Future;
import java.util.concurrent.LinkedBlockingQueue;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;

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location xxxx {
  proxy_pass        http://localhost:9000;
  proxy_buffering off;
  proxy_cache off;
  proxy_redirect    off;
  proxy_set_header Connection '';
  proxy_http_version 1.1;
  chunked_transfer_encoding off;
  proxy_set_header  Host $host;
  proxy_set_header  X-Real-IP  $remote_addr;

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'''
rate_limit2.py

Copyright 2014, Josiah Carlson - [email protected]
Released under the MIT license

This module intends to show how to perform standard and sliding-window rate
limits as a companion to the two articles posted on Binpress entitled
"Introduction to rate limiting with Redis", parts 1 and 2:

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Scaling your API with rate limiters

The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.

In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.

Request rate limiter

This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.

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public void testFlattenMap() {
    Map<String, Map<String, Long>> counters = new HashMap<>();

    Map<String, Long> nested = new HashMap<>();
    nested.put("inner1", 1L);
    nested.put("inner2", 2L);
    nested.put("inner3", 3L);

    counters.put("out1", nested);
    counters.put("out2", nested);

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package com.meiliao.ops.utils.security;

import java.security.NoSuchAlgorithmException;
import java.security.NoSuchProviderException;
import java.security.SecureRandom;
import java.util.HashMap;
import java.util.Map;
import java.util.Queue;
import java.util.concurrent.ConcurrentLinkedQueue;

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在Mac、Linux 终端显示 Git 当前所在分支

1. 进入你的home目录

cd ~

1. 编辑.bashrc文件

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#!/usr/bin/env python
# encoding: utf-8

from __future__ import print_function

'''
analyze /proc/<pid>/smaps

doc
http://liutaihua.github.io/2013/04/25/process-smaps-analysis.html