CYBER FRAUD APP DETECTION USING MACHINE LEARNING

Abstract

The rapid growth of digital transactions and mobile
applications has significantly increased the risk of
cyber fraud, making security a critical concern for
individuals and organizations. Traditional fraud
detection systems, which rely on rule-based
mechanisms, often fail to adapt to evolving fraud
patterns and result in delayed or inaccurate
detection. This project presents a machine learningbased
Cyber Fraud App Detection System designed
to identify fraudulent applications and suspicious
activities efficiently. The system utilizes supervised
learning algorithms such as Logistic Regression,
Random Forest, and Gradient Boosting to analyze
application features including permissions, API
calls, behavioral patterns, and metadata. Data
preprocessing techniques such as normalization,
feature extraction, and transformation are applied
to ensure high-quality input for model training. The
trained model classifies applications as fraudulent
or legitimate and provides confidence scores for
prediction accuracy. The system is designed to
operate in real time, enabling fast detection and
alert generation while ensuring data privacy
through local processing. Additionally, a userfriendly
interface is integrated to allow users to
input data, view results, and analyze fraud patterns
visually. Experimental results demonstrate
improved accuracy, reduced false positives, and
efficient performance on large datasets. The
proposed system offers a scalable, cost-effective,
and reliable solution for detecting cyber fraud in
modern digital environments.