An Intelligent System for Detecting Malicious Activity and Financial Fraud: A Programmatic Approach to Fraud Prevention

Abstract

In today’s digital age, we are almost constantly connected to the internet and rely heavily on various websites for activities such as e-commerce, education, entertainment, and gaming. However, many users often overlook a critical aspect—whether the websites they interact with are genuinely secure. With the increasing number of online scams and fraudulent activities, it is essential to have tools that ensure safe browsing and transaction practices. Our proposed solution is a smart website that assists users in identifying potentially harmful or fraudulent platforms, especially in high-risk sectors like e-commerce, banking, and fintech. The system will utilize advanced analysis of user behavior, access patterns, and historical data to detect malicious intent or fraud attempts. By leveraging these insights, our program will alert users in real time with warnings if they attempt to access suspicious websites. This proactive approach aims to significantly reduce the risk of financial loss and enhance user trust in online platforms. By automatically providing suggestions and alerts, users are better equipped to avoid scams. Ultimately, our solution not only empowers individuals to browse safely but also acts as a critical tool in combating cyber fraud in an increasingly digital future.

Introduction

With increasing digitalization, fake websites have become a significant cybersecurity threat, tricking users into revealing sensitive information and causing financial and reputational damage. This paper investigates how cybercriminals exploit fake websites, identifies key features to detect them—such as suspicious domain names, design flaws, and misleading information—and emphasizes the need for early detection to protect users and organizations.

Traditional blacklist-based detection methods are limited by their reactive nature, so this study proposes leveraging machine learning and deep learning to create proactive, real-time phishing website detection systems. Machine learning models like Random Forest and Artificial Neural Networks have demonstrated high accuracy (up to 97%) in classifying phishing sites.

Literature reviews highlight the effectiveness of URL classification and behavioral analysis in identifying malicious websites, including advanced threats like malicious web crawlers that mimic human browsing.

A user survey revealed that while many are aware of the risks, a large portion do not consistently verify websites before use, increasing vulnerability. Users demand high accuracy (90-100%) from detection tools, underscoring the need for reliable cybersecurity solutions.

Finally, the paper describes the implementation of a Random Forest-based phishing detection model using Python libraries such as pandas and scikit-learn to build, train, and evaluate the system.

Conclusion

In this study, we successfully examined various techniques and methodologies employed by scammers to carry out fraudulent activities. By applying our detection methods, we identified common scam patterns including fake reviews, fraudulent email addresses, transaction fraud, and fake contact numbers. Our approach involved implementing a multi-layered fraud detection system, which enhanced the overall accuracy of identifying malicious behavior while significantly reducing false positives. This comprehensive strategy allowed us to better distinguish genuine activities from deceptive ones, making the detection process more reliable. The results demonstrate that combining multiple detection techniques can strengthen defenses against scams and improve the effectiveness of fraud prevention efforts. This research contributes valuable insights for developing more robust security solutions to protect users from increasingly sophisticated fraudulent schemes. The implemented model demonstrates how simple URL-based features, when paired with a reliable machine learning algorithm like Random Forest, can effectively distinguish between legitimate and fraudulent websites.While the dummy dataset used here is minimal, the concept can be extended to large, real-world datasets forBuilding browser extensionsIntegrating with firewalls, Creating user alert systems. With real-world phishing data and more advanced features (like domain age, WHOIS info, or page content analysis), this approach can become a powerful tool in automated fraud detection systems.

References

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Copyright

Copyright © 2025 Ms. Sayali Parab, Mr. Chayan Bhattacharjee. 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.