Beyond the Surface: Deep Dive into Fraud Detection Technologies and Strategies for Robust Application Security

Abstract

Purpose: With the increasing use of mobile applications and the rise in fraudulent activities, this study examines the importance of effective fraud detection software. It highlights the need for a multi-layered approach to effectively identify and mitigate fraudulent apps.

Design/Methodology/Approach: The detection software employs static and dynamic analysis techniques. Using advanced tools, static analysis examines the app’s codebase for vulnerabilities, insecure coding practices, and potential backdoors. Dynamic analysis involves executing the app in a controlled environment to observe its operations and detect unauthorized data access or suspicious network activity. Additionally, the software incorporates analysis of usage patterns to identify deviations from typical patterns and uses signature-based detection to compare app functions with known fraud patterns. Machine learning algorithms further improve detection accuracy by learning from new threats and adapting to emerging fraud techniques. Alerts and actionable insights allow for prompt responses to potential risks. Cloud-based analytics aggregate data from various sources to enhance overall detection capability and response time. The software is also designed to be compatible with different mobile operating systems and app environments.

Findings: The multi-layered approach effectively tackles both existing and new threats. By integrating static and dynamic analyses with pattern-based detection and machine learning, the software supports a secure mobile ecosystem, protecting user data and ensuring app integrity.

Originality/Value: This comprehensive fraud detection strategy offers a robust solution by combining various analysis techniques and adapting to evolving threats. It provides developers and users with effective tools to manage potential risks and maintain a secure mobile environment.

Paper Type: Theme Based Paper