Phishing attacks are among the most common cybersecurity threats, tricking users through deceptive emails, websites, and messages. This project leverages Machine Learning (ML) and API to detect phishing attempts and classify URLs as legitimate or fraudulent. Project also provides various cybersecurity tools packed in one website.
✅ URL Analysis: Identifies suspicious website links.
✅ Machine Learning Models: Uses supervised learning techniques for classification.
✅ Real-time Detection: Offers instant classification results.
✅ Hash Generator & Verifier: Generates and verifies hashes (SHA-256, SHA-1, MD-5).
✅ Image Metadata Remover and Reader: remove/read metadata from images.
✅ Base64 Tool (Encode/Decode): Encodes and decodes data to and from Base64 format easily.
- 🐍 Programming Language: Python
- 📚 Machine Learning Libraries: scikit-learn, pandas, numpy
- 🌐 Web Scraping: BeautifulSoup, requests
- 📊 Dataset: Publicly available phishing datasets (e.g., PhishTank, UCI ML Repository, VirusTotal)
1️⃣ Clone the repository:
bash git clone https://github.com/shamak24/FishKat.git