FuzzuTech Projects

━━━━━━━━━━━━━━━━━━━━ 📖 Introduction In this tutorial, we will build a modern Python AI Encryption and Data Analyzer GUI using Python and PyQt6. The project is called CRYPTBREAK AI . It is an educational cybersecurity application that uses an AI API to analyze user-provided encoded, hashed, obfuscated, or encryption-like data and explain what type of data it appears to be. The application includes an AI-powered analysis system, automatic demo data generation, Base64 detection, Hex decoding, URL encoding detection, ROT13 analysis, Caesar Cipher demonstrations, MD5 detection, SHA-1 detection, SHA-256 detection, random ciphertext generation, confidence scoring, security explanations, and a recording-friendly portrait interface. The application is designed especially for learning Python GUI development, PyQt6 application development, AI API integration, cybersecurity concepts, data encoding, hashing, weak classical ciphers, and the difference between encoding, h...

━━━━━━━━━━━━━━━━━━━━ 📖 Introduction Welcome to Zero to Hero – Ethical Hacking with Python (Windows Edition) . In this beginner-friendly cybersecurity series, you will learn how ethical hackers think, how Python is used in modern cybersecurity, and how to build real-world defensive security tools completely from scratch. Episode 1 focuses on the fundamentals of ethical hacking, cybersecurity concepts, Python programming, safe learning practices, and the complete roadmap of this professional course. This series is specially designed for students, developers, cybersecurity enthusiasts, and beginners who want to enter the cybersecurity field using Python. Throughout this course, you will build practical desktop applications using Python and CustomTkinter, learn cybersecurity concepts through real-world examples, and understand how professional security tools work from a defensive perspective. Unlike traditional theory-based courses, every episode includes prac...

  Demo : Click Video 👇👇👇 Features: ✅ Embedded YouTube Video ✅ Keywords for SEO ✅ Tags from above ✅ Attractive thumbnail Code : # iot_federated_rl_gui.py # Single-file: Python – IoT & Autonomous Protection with Federated Learning + RL (CustomTkinter GUI) # Author: FuzzuTech # Notes: # - No external ML frameworks used; lightweight FedAvg + Logistic Regression (from scratch) using NumPy. # - RL via tabular Q-learning with epsilon-greedy policy. # - Live simulator of IoT traffic + attack scenarios (Normal, PortScan, BruteForce, DDoS). # - Actions: Block, Throttle, Isolate, Honeypot. Reward balances damage vs. mitigation cost. # - GUI: CustomTkinter with tabs (Dashboard, Simulator, Federated Learning, Logs, Settings). import sys import time import json import threading import random from collections import deque, defaultdict import numpy as np import tkinter as tk import customtkinter as ctk from tkinter import messagebox, filedialog from matplotlib.figure import Figure ...

  Demo : Click Video 👇👇👇 Features : Python + CTkinter GUI Real-time Sentiment Detection (VADER + Naive Bayes) Dark/Light Themes Emoji-powered results Export CSV History Train/Load ML Models Code : import os import json import time import tkinter as tk from tkinter import filedialog, messagebox import customtkinter as ctk # Data / ML import nltk from nltk.sentiment import SentimentIntensityAnalyzer import pandas as pd import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report from joblib import dump, load APP_TITLE = "CTk Sentiment Analyzer — FuzzuTech" ctk.set_appearance_mode("Dark") ctk.set_default_color_theme("blue") # Ensure data/model dirs os.makedirs("data", exist_ok=True...