Phish Catcher and Web Spoofing Attack Using Machine Learning

Abstract

ThePhishing threats are one of the major evolving threats that breaks confidentiality and causes serious risk. They frequently serve as entrance points for a variety of cyberattacks, such as money fraud, malware distribution, and data theft. Phishing attacks are now emerging with more advanced tactics and technologies. Because we are mainly rely on manual feature engineering, traditional detection techniques have difficulty keeping up with emerging and complex phishing tactics like zero-day assaults. By automating feature extraction and enhancing flexibility, Machine learning (ML), Random forest classifier (RFC) and deep learning (DL) present a possible answer. These sophisticated algorithms improve detection accuracy while also better addressing new phishing techniques. The main objective of this study is to use ML algorithms to detect and predict the fake websites and reduce phishing attacks.

Introduction

Phish Catcher is an advanced machine learning-based system designed to detect and prevent phishing and web spoofing attacks, which involve creating fake websites to steal sensitive information. It uses supervised learning and feature extraction to analyze website attributes—such as URL patterns, domain features, and content structure—to classify sites as legitimate or malicious. The system continuously learns from new threats, improving detection accuracy and protecting users from evolving cyberattacks.

Objectives:
The project aims to build an intelligent, automated, and scalable system that detects phishing attacks in real time using machine learning, overcoming the limitations of traditional rule-based systems. The system adapts dynamically to new attack patterns to enhance online security.

Proposed Work:
Phish Catcher employs a multi-layered approach combining feature extraction from real-time web traffic and phishing datasets, with machine learning models like Random Forest, Gradient Boosting, and Neural Networks to classify websites accurately. It features continuous real-time monitoring and a user-friendly interface for URL investigation.

System Architecture:
The system includes a client-side browser extension for feature extraction and local inference, supplemented by a backend server for data collection, model training, and updates. Key design goals include performance, accuracy, scalability, security, and privacy.

Methodology:

• Dataset Collection: Phishing data from Phish Tank is preprocessed by cleaning, shuffling, and balancing before training/testing.

• Data Preprocessing: Raw data is cleaned, tokenized, normalized, and encoded to highlight phishing patterns.

• Feature Extraction: Important features include URL length, suspicious keywords, domain age, HTTPS usage, HTML elements like hidden iframes, JavaScript patterns, server details, user behavior, and content analysis.

• Model Implementation: Uses classifiers like Decision Trees and Random Forests, leveraging features to split data and classify sites as phishing or legitimate.

Results:
Phish Catcher was tested against real web scenarios, showing effective classification of legitimate versus phishing URLs by integrating multiple features and machine learning techniques.

Conclusion

This project was able to effectively prove the usefulness and viability of employing machine learning algorithms in identifying phishing and web spoofing attacks. Using [list particular algorithms utilized, e.g., Random Forest, Support Vector Machines, Deep Learning models], we were successful in creating a model that attained [list performance measures, e.g., high accuracy, precision, recall, F1-score] in classifying legitimate and malicious sites on the basis of features derived from URLs, HTML content, and other pertinent information. The deployment of near-real-time detection features demonstrated the system\'s capabilities to actively ward off users against cyber threats in real time. The findings establish the significance of machine learning technology in the war against the continuous evolution of cyberattacks. Lastly, the extraction of feature importance gave useful feedback on the distinguishing features that significantly relate to spoofing and phishing attempts, with which more accurate defense mechanisms may be developed. Future development should be aimed at building a dynamically adaptive system through the use of real-time feature extraction, taking advantage of sophisticated methods such as JavaScript analysis, network monitoring, and visual similarity detection, and integrating with browser extensions and security software for instant user protection. Strengthening the model\'s resilience by doing adversarial training and anomaly detection and adding explainable AI for improved user confidence and enabling collaboration on data sharing through mechanisms like blockchain will guarantee that the system stays effective in counteracting advanced phishing threats.

References

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Copyright

Copyright © 2025 Mrs. Nazeema S, Ms. Abinaya D, Ms. Inika R K, Ms. Nandhini S, Ms. Akshaya E. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.