AI Phishing Detector GUI in Python | Cyber Security Project with Source Code
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📖 Introduction
In this complete Python cybersecurity tutorial, we will build an AI Phishing Detector GUI, a modern desktop security application using Python and CustomTkinter.
The AI Phishing Detector allows users to enter suspicious URLs, analyze common phishing indicators, calculate a phishing risk score, classify links as Safe, Suspicious, or Dangerous, view detailed security analysis reports, maintain scan history, and export security reports.
The application performs heuristic and rule-based URL analysis using HTTPS detection, IP address detection, URL length analysis, suspicious keyword detection, URL shortener identification, punycode detection, suspicious domain extension analysis, excessive subdomain detection, encoded character analysis, unusual query parameter detection, and redirection pattern checks.
This Python cybersecurity project is perfect for learning Python GUI development, CustomTkinter desktop application development, URL parsing, phishing detection concepts, cybersecurity heuristics, risk scoring systems, security report generation, scan history management, and defensive security application development.
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✨ Features
✅ Modern CustomTkinter Cybersecurity GUI
✅ Professional Dark Security Dashboard
✅ Suspicious URL Scanner
✅ Automatic URL Normalization
✅ URL Parsing and Hostname Extraction
✅ HTTPS Security Check
✅ Direct IP Address Detection
✅ URL Length Analysis
✅ Extremely Long URL Detection
✅ Suspicious Keyword Detection
✅ URL Shortening Service Detection
✅ Punycode Domain Detection
✅ Possible Homograph Attack Warning
✅ Suspicious Domain Extension Detection
✅ Excessive Subdomain Detection
✅ Excessive Hyphen Detection
✅ Unusual Number Detection in Domains
✅ Encoded Character Detection
✅ Excessive Query Parameter Detection
✅ At Symbol Detection
✅ Multiple HTTP Reference Detection
✅ URL Redirection Pattern Analysis
✅ Phishing Risk Scoring System
✅ Risk Score from 0 to 100
✅ Safe URL Classification
✅ Suspicious URL Classification
✅ Dangerous URL Classification
✅ Detailed Security Analysis Report
✅ Security Recommendations
✅ Detected Domain Display
✅ Security Status Display
✅ Scan Date and Time Tracking
✅ Scan History System
✅ Stores Up to 50 Recent Scan Results
✅ Clear Scanner Function
✅ Export Security Report as Text File
✅ Keyboard Enter Key Scanning Support
✅ No API Key Required
✅ Single-File Python Desktop Application
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🎥 Full Video Tutorial
Watch the complete AI Phishing Detector GUI Python Project tutorial below 👇
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🛠 Technologies Used
• Python
• Tkinter
• CustomTkinter
• Python urllib.parse Module
• Python urlparse Function
• Python IPAddress Module
• Python Regular Expressions
• Python DateTime Module
• Tkinter File Dialog
• Tkinter MessageBox
• URL Parsing
• Hostname Extraction
• Heuristic URL Analysis
• Rule-Based Phishing Detection
• Cybersecurity Risk Scoring
• Security Report Generation
• Scan History Management
• Desktop GUI Development
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🔐 How AI Phishing Detector Works
When the AI Phishing Detector starts, the user enters a suspicious website URL into the URL Scanner.
If the entered URL does not contain an HTTP or HTTPS protocol, the application automatically normalizes the URL before starting the security analysis.
The application parses the URL and extracts important components including the protocol, hostname, path, and query parameters.
The phishing detection engine performs multiple heuristic and rule-based security checks on the URL.
The application checks whether the website uses HTTPS encryption and detects URLs that directly use an IP address instead of a normal domain name.
The detector analyzes URL length, suspicious keywords, excessive subdomains, excessive hyphens, encoded characters, risky domain extensions, URL shortening services, punycode domains, unusual query parameters, redirection patterns, and multiple HTTP references.
Every suspicious indicator adds points to the phishing risk score according to its potential security risk.
After all security checks are completed, the final risk score is limited to a maximum value of 100.
URLs with a score from 0 to 20 are classified as Safe, scores from 21 to 50 are classified as Suspicious, and scores above 50 are classified as Dangerous.
The final result displays the security status, phishing risk score, detected domain, individual security findings, and security recommendations.
Every completed scan is automatically added to the Scan History section, where users can review previous URLs, security classifications, risk scores, domains, and scan times.
Users can also export the currently displayed security analysis report as a text file for future reference.
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📊 Phishing Risk Scoring System
The AI Phishing Detector uses a simple and understandable risk scoring system to classify suspicious URLs.
Missing HTTPS encryption, unusually long URLs, suspicious keywords, excessive subdomains, risky domain extensions, encoded characters, and other suspicious URL characteristics increase the phishing risk score.
High-risk indicators such as direct IP address usage, punycode domains, the at symbol, and multiple HTTP references can significantly increase the final score.
The final score is classified into three security levels:
🟢 0–20 Risk Score — SAFE
🟡 21–50 Risk Score — SUSPICIOUS
🔴 51–100 Risk Score — DANGEROUS
This risk scoring system helps users quickly understand how many common phishing indicators were detected inside the analyzed URL.
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🛡 Security Checks Performed
• HTTPS Protocol Analysis
• Direct IP Address Detection
• URL Length Analysis
• At Symbol Detection
• Multiple Slash Redirection Detection
• Excessive Domain Hyphen Detection
• Excessive Subdomain Detection
• Suspicious Keyword Analysis
• URL Shortening Service Detection
• Punycode Domain Detection
• Suspicious Domain Extension Detection
• Unusual Digit Count Detection
• Encoded Character Detection
• Excessive Query Parameter Detection
• Multiple HTTP Reference Detection
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📸 Screenshots
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📚 Step-by-Step Tutorial
Step 1 — Install Python and CustomTkinter
Step 2 — Import CustomTkinter and Tkinter Modules
Step 3 — Import URL Parsing and Security Modules
Step 4 — Configure the Application Appearance and Theme
Step 5 — Create the PhishingDetector Security Engine Class
Step 6 — Create the Suspicious Keyword Database
Step 7 — Add URL Shortening Service Detection Rules
Step 8 — Add Suspicious Domain Extension Detection Rules
Step 9 — Build the URL Normalization Function
Step 10 — Create the IP Address Detection Function
Step 11 — Build the Main URL Analysis Method
Step 12 — Add HTTPS Protocol Detection
Step 13 — Add Direct IP Address Detection
Step 14 — Create URL Length Analysis
Step 15 — Add At Symbol and Redirection Pattern Detection
Step 16 — Analyze Excessive Hyphens and Subdomains
Step 17 — Build Suspicious Keyword Detection
Step 18 — Add URL Shortener Detection
Step 19 — Add Punycode and Homograph Attack Warning
Step 20 — Add Suspicious Domain Extension Detection
Step 21 — Analyze Unusual Digits and Encoded Characters
Step 22 — Add Query Parameter Anomaly Detection
Step 23 — Add Multiple HTTP Reference Detection
Step 24 — Create the 0 to 100 Risk Scoring System
Step 25 — Create Safe, Suspicious, and Dangerous Classifications
Step 26 — Build the Main CustomTkinter Application Window
Step 27 — Create the Cybersecurity Sidebar Navigation
Step 28 — Build the URL Scanner Input Interface
Step 29 — Create the Security Status and Risk Score Cards
Step 30 — Build the Detailed Security Analysis Report Interface
Step 31 — Connect the URL Scanner to the Detection Engine
Step 32 — Display Security Findings and Recommendations
Step 33 — Create the Scan History System
Step 34 — Add the Clear Scanner Function
Step 35 — Create the Export Security Report Function
Step 36 — Add Keyboard Enter Key Scanning Support
Step 37 — Run and Test the AI Phishing Detector
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💻 Full Source Code Available on GitHub 👇
🔗 View AI Phishing Detector GUI Full Source Code on GitHub
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⚠️ Educational Purpose Disclaimer
The AI Phishing Detector GUI is created for educational and defensive cybersecurity purposes. This Python project demonstrates heuristic URL analysis, phishing indicator detection, cybersecurity risk scoring, URL parsing, security report generation, scan history management, and defensive desktop application development.
Use this project only to analyze URLs and systems that you are authorized to test. Do not use this application to access malicious websites, steal credentials, perform unauthorized security testing, or conduct illegal activities.
This application uses heuristic and rule-based URL analysis. It does not use a trained machine learning model, live threat intelligence database, real-time domain reputation service, website content inspection, or professionally audited phishing detection infrastructure.
A low phishing risk score does not guarantee that a website is safe. Always verify suspicious links using trusted security services and avoid entering passwords, banking details, OTP codes, or personal information on unknown websites.
The AI Phishing Detector GUI is a learning project and should not be considered a replacement for professional antivirus software, enterprise threat detection platforms, browser security systems, or production cybersecurity solutions.
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🎯 Conclusion
The AI Phishing Detector GUI is a complete Python cybersecurity project for developers who want to learn CustomTkinter GUI development, URL parsing, heuristic phishing detection, cybersecurity risk scoring, suspicious URL analysis, security report generation, scan history management, report exporting, and modern desktop application development.
If you enjoy unique Python projects, cybersecurity applications, modern GUI projects, Python security tools, secure coding tutorials, and practical desktop applications, subscribe to FuzzuTech and explore more Python projects.
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