Ddos Collection - MrZx

Beta release browser only 10$ with local ai (Can Detect Protection) feature:(Only Cf Captcha)

—reconnection —connection —browser —flood —emulation —dratelimit

demo: demo attack dstat Alpha release browser only 50$ with local ai (Recaptcha Ai, Captcha Text Ai, Protection Detect) feature: (Recaptcha, turnstile, Captcha Text(Costum Element))

—reconnection —connection —browser —flood —emulation —dratelimit —protect

++:

Free Module Bypass Free 80% Dataset Recaptcha Free 95% Dataset Captcha Text Free update 2x

demo Captcha Text: demo demo Recaptcha: Soon

I have succeeded in creating an "ai" model that can complete the captcha text challenge with a 95% accuracy rate and this ai can complete it quickly Note:

I will give free code with beta version using tesseract js can solve 50%, 5heart reactions i will share

@mscjs @fly1x @jo1el

from PIL import Image

def split_image(image_path, grid_size=(3, 3), save=False):
    image = Image.open(image_path)
    width, height = image.size
    grid_width = width // grid_size[0]
    grid_height = height // grid_size[1]

    images = []
    for i in range(grid_size[0]):
        for j in range(grid_size[1]):
            left = i * grid_width
            upper = j * grid_height
            right = left + grid_width
            lower = upper + grid_height

            cropped_img = image.crop((left, upper, right, lower))
            images.append(cropped_img)

            if save:
                cropped_img.save(f"grid_{i}_{j}.png")
    
    return images

image_path = "3x3.png"

split_images = split_image(image_path, grid_size=(3, 3), save=True)

for idx, img in enumerate(split_images):
    print(f"Grid {idx+1}: {img.size}")

this code can split images recaptcha v2 This makes it easier to complete recaptcha v2 @mscjs @fly1x @jo1el

soon i will send demo ai with 95% can solve captcha text with model onnx

I will apply this stresser application again with various features. give support with this reaction chat ☕️

Go 300 subs will share c simple flood code

const cheerio = require('cheerio');
const natural = require('natural');
const tokenizer = new natural.WordTokenizer();
const TfIdf = natural.TfIdf;
const tfidf = new TfIdf();

const rateLimitKeywords = ['too many requests', 'rate limit', 'exceeded limit', 'slow down', 'retry later', 'temporarily unavailable'];

const checkSimilarity = (text) => {
  let isRateLimited = false;
  tfidf.addDocument(text);

  rateLimitKeywords.forEach((keyword) => {
    tfidf.tfidfs(keyword, function(i, measure) {
      if (measure > 0.1) {
        isRateLimited = true;
      }
    });
  });

  return isRateLimited;
};

const detectRateLimit = (htmlContent) => {
  const $ = cheerio.load(htmlContent);
  const bodyText = $('body').text().toLowerCase();
  const titleText = $('title').text().toLowerCase();
  const h1Text = $('h1').text().toLowerCase();
  const pText = $('p').text().toLowerCase();

  const combinedText = `${bodyText} ${titleText} ${h1Text} ${pText}`;
  
  const tokens = tokenizer.tokenize(combinedText);

  const keywordDetected = rateLimitKeywords.some(keyword => {
    return tokens.join(' ').includes(keyword);
  });

  const tfidfResult = checkSimilarity(combinedText);

  return keywordDetected || tfidfResult;
};

Caller:

const isRateLimited = detectRateLimit(/*response data*/);
    
    if (isRateLimited) {
      console.log('Ratelimit detect [429].');
    }

The detectRateLimit(htmlContent) function analyzes the HTML content by checking for specific keywords related to rate limits and uses Tf-Idf to assess the semantic relevance. If any indicators are found, it prints a message indicating that a rate limit has been detected. ch: @jo1el cr: @mscjs

const slider = await page.waitForSelector('#slider');
    const sliderContainer = await page.$('#sliderContainer');
elemen container
    const containerBox = await sliderContainer.boundingBox();

    await page.mouse.move(containerBox.x + 10, containerBox.y + containerBox.height / 2);
    await page.mouse.down();
    await page.mouse.move(containerBox.x + containerBox.width - 20, containerBox.y + containerBox.height / 2, { steps: 20 });
    await page.mouse.up();

puppeteer code to perform automatic element shifting auto sliderrr cr: @mscjs ch: @jo1el

const cluster = require('cluster');
const os = require('os');
const brain = require('brain.js');

const trainingData = [
  { input: { cpus: 2, load: 0.1, memory: 0.1, ioIntensity: 0.2, networkLoad: 0.1 }, output: [1] },
  { input: { cpus: 2, load: 0.3, memory: 0.2, ioIntensity: 0.3, networkLoad: 0.2 }, output: [1] },
  { input: { cpus: 2, load: 0.5, memory: 0.5, ioIntensity: 0.4, networkLoad: 0.3 }, output: [2] },
  { input: { cpus: 2, load: 0.7, memory: 0.6, ioIntensity: 0.5, networkLoad: 0.4 }, output: [2] },
  { input: { cpus: 2, load: 0.9, memory: 0.8, ioIntensity: 0.7, networkLoad: 0.6 }, output: [3] },
  { input: { cpus: 4, load: 0.1, memory: 0.1, ioIntensity: 0.2, networkLoad: 0.1 }, output: [1] },
  { input: { cpus: 4, load: 0.3, memory: 0.2, ioIntensity: 0.3, networkLoad: 0.2 }, output: [2] },
  { input: { cpus: 4, load: 0.5, memory: 0.4, ioIntensity: 0.4, networkLoad: 0.3 }, output: [3] },
  { input: { cpus: 4, load: 0.7, memory: 0.6, ioIntensity: 0.6, networkLoad: 0.4 }, output: [4] },
  { input: { cpus: 4, load: 0.9, memory: 0.8, ioIntensity: 0.8, networkLoad: 0.6 }, output: [5] },
  { input: { cpus: 8, load: 0.1, memory: 0.2, ioIntensity: 0.2, networkLoad: 0.1 }, output: [2] },
  { input: { cpus: 8, load: 0.3, memory: 0.3, ioIntensity: 0.4, networkLoad: 0.2 }, output: [4] },
  { input: { cpus: 8, load: 0.5, memory: 0.5, ioIntensity: 0.5, networkLoad: 0.3 }, output: [5] },
  { input: { cpus: 8, load: 0.7, memory: 0.7, ioIntensity: 0.6, networkLoad: 0.5 }, output: [6] },
  { input: { cpus: 8, load: 0.9, memory: 0.9, ioIntensity: 0.8, networkLoad: 0.7 }, output: [7] },
  { input: { cpus: 16, load: 0.1, memory: 0.2, ioIntensity: 0.3, networkLoad: 0.1 }, output: [3] },
  { input: { cpus: 16, load: 0.3, memory: 0.4, ioIntensity: 0.4, networkLoad: 0.3 }, output: [6] },
  { input: { cpus: 16, load: 0.5, memory: 0.5, ioIntensity: 0.6, networkLoad: 0.5 }, output: [8] },
  { input: { cpus: 16, load: 0.7, memory: 0.7, ioIntensity: 0.7, networkLoad: 0.6 }, output: [10] },
  { input: { cpus: 16, load: 0.9, memory: 0.9, ioIntensity: 0.9, networkLoad: 0.8 }, output: [12] },
  { input: { cpus: 32, load: 0.1, memory: 0.3, ioIntensity: 0.2, networkLoad: 0.1 }, output: [4] },
  { input: { cpus: 32, load: 0.3, memory: 0.4, ioIntensity: 0.5, networkLoad: 0.2 }, output: [8] },
  { input: { cpus: 32, load: 0.5, memory: 0.6, ioIntensity: 0.6, networkLoad: 0.4 }, output: [12] },
  { input: { cpus: 32, load: 0.7, memory: 0.8, ioIntensity: 0.7, networkLoad: 0.6 }, output: [16] },
  { input: { cpus: 32, load: 0.9, memory: 0.9, ioIntensity: 0.9, networkLoad: 0.8 }, output: [20] },
  { input: { cpus: 64, load: 0.1, memory: 0.2, ioIntensity: 0.2, networkLoad: 0.2 }, output: [6] },
  { input: { cpus: 64, load: 0.3, memory: 0.4, ioIntensity: 0.4, networkLoad: 0.3 }, output: [12] },
  { input: { cpus: 64, load: 0.5, memory: 0.5, ioIntensity: 0.5, networkLoad: 0.5 }, output: [16] },
  { input: { cpus: 64, load: 0.7, memory: 0.7, ioIntensity: 0.7, networkLoad: 0.7 }, output: [20] },
  { input: { cpus: 64, load: 0.9, memory: 0.9, ioIntensity: 0.8, networkLoad: 0.8 }, output: [24] }
];

const net = new brain.NeuralNetwork();
net.train(trainingData);

const numCPUs = os.cpus().length;
const currentLoad = 0.7;
const predictedWorkers = Math.round(net.run({ cpus: numCPUs, load: currentLoad })[0]);

if (cluster.isMaster) {
  console.log(`Master process ${process.pid} is running`);

  for (let i = 0; i < predictedWorkers; i++) {
    cluster.fork();
  }

  cluster.on('exit', (worker, code, signal) => {
    console.log(`Worker ${worker.process.pid} died`);
  });
} else {
  console.log(`Worker ${process.pid} started`);

  const http = require('http');
  http.createServer((req, res) => {
    res.writeHead(200);
    res.end('kontol\n');
  }).listen(8000);
}

Ai logic auto chooses the best threads to use on cluster module @jo1el