INTRODUCTION & PROBLEM STATEMENT

The Phishing Threat

Phishing attacks remain one of the most prevalent cybersecurity threats, with over 300,000 unique phishing sites detected monthly worldwide.

Annual Loss

$17.5B+

Success Rate

32%

Detection Time

12 hrs

The Challenge

  • Phishing sites are becoming increasingly sophisticated and harder to detect visually
  • Traditional blacklist approaches have significant time delays
  • Manual verification is not scalable to the volume of threats
  • Need for automated, accurate, real-time detection systems

Our Solution

An end-to-end Machine Learning pipeline to detect phishing URLs from network security data with high accuracy and real-time processing capabilities.

ML-Based Detection

Leverages 30 URL & website features for phishing classification

Real-Time Analysis

Batch & real-time prediction endpoints via FastAPI

Cloud-Native

Deployed on AWS with containerization & auto-scaling

Full MLOps Pipeline

Automated data processing, training, validation & deployment

Project Goal: Build an automated, production-ready phishing detection system with high accuracy, comprehensive MLOps practices, and cloud-native deployment.

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