Cyber Fraud App Detection and Blocking System using Machine Learning
DOI:
https://doi.org/10.15662/IJARCST.2025.0806016Keywords:
Machine Learning, URL Classification, Real-time Fraud Detection, DNS Blocking CybersecurityAbstract
In today's digital world, cyber fraud through harmful mobile applications is a serious threat to user privacy and national security. This research introduces a Cyber Fraud App Detection and Blocking System that uses machine learning to spot and flag fraudulent URLs or app links in real time. The system pulls features like domain age, the presence of suspicious keywords, extension types, WHOIS details, and DNS status to assess whether an app-related URL is legitimate. A trained machine learning model, based on ensemble classifiers, classifies the input as either safe or fraudulent. An admin dashboard is also included to manually block or unblock URLs, monitor logs, and review user-reported threats. Users can interact with the platform by submitting suspicious detections via email, which are logged for admin review. The system provides high detection rates with the real-time performance retained, so it is suitable for large-scale cybersecurity environments. The solution gives real-time feedback to users and helps cybersecurity managers make optimal decisions. Experimental results confirm the effectiveness of the approach provides instant feedback to the users and enables optimal decisions for cybersecurity managers. Experimental outcomes verify the efficacy of the method in detecting and eliminating cyber fraud attacks.
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