Phishing attacks continue to be one of the most prevalent and dangerous cybersecurity threats in today’s digital ecosystem. These attacks exploit human trust by impersonating legitimate organizations, leading to financial loss, identity theft, credential compromise, and large-scale data breaches.
With the rapid growth of online banking, e-commerce, and digital governance, phishing websites have become increasingly sophisticated, often bypassing traditional blacklist-based and signature-based security mechanisms.
This project is inspired by the AI Challenge Problem Statement (PS-2), which emphasizes the detection of phishing websites using intelligent and scalable techniques. The challenge highlights the need for solutions that are not only accurate but also transparent and explainable.
Most modern detection systems rely heavily on machine learning models that act as black boxes. While effective, these systems lack interpretability, making them unsuitable for academic evaluation, regulatory environments, and trust-critical applications.
The core problem addressed by this project is the design of a phishing detection system that can accurately identify malicious websites while clearly explaining why a website is classified as phishing, suspicious, or legitimate.
The system must analyze multiple characteristics of a website such as URL structure, domain properties, transport-level indicators, identity deception patterns, and semantic intent, without relying on opaque decision-making models.
The primary objectives of this project are:
The proposed solution is designed for academic research, educational demonstrations, and foundational cybersecurity studies. It serves as a baseline system that can later be extended with advanced infrastructure analysis or hybrid AI techniques while preserving explainability.