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