# Advanced Financial Fraud Malware Detection Method
## 1 Introduction
* Billing fraud: Malicious apps that exploit payment systems without user consent, often through Android payment methods or unauthorized transactions [16]. * Stalkerware: Software installed on a user device without knowledge, enabling third parties to track location, monitor communication, and access personal data [17]. * Hostile downloaders: Code that does not directly cause harm but downloads other unwanted or malicious software onto the device [18]. * Phishing: These apps disguise themselves as trusted sources, tricking users into submitting personal and billing information, which is then sent to malicious third parties [19]. * Spyware: Malicious software that monitors the online activity of a user and collects sensitive information, such as login credentials, banking details, and private data without the consent of the user [20]. In this paper, we present a machine learning (ML) model that leverages static analysis to detect financial fraud malware. ML is well suited for this task because it excels in identifying intricate patterns and generalizing from past data, making it ideal for recognizing evolving malware strategies. Static analysis, which involves examining the code and structure of an app without execution, provides an efficient and safe method to extract features such as permissions, application programming interface (API) calls, and code patterns. The proposed ML model uses static analysis to rapidly analyze large datasets of apps and detect financial fraud malware before execution, thereby minimizing security risks. This approach combines scalability with the ability to detect new and previously undiscovered malware strains. The main contributions of this study are as follows:  
* Static Analysis for Financial Fraud Malware Detection: This study proposes a novel approach utilizing static analysis for detecting financial fraud malware. Compared to dynamic analysis, static analysis is more efficient for real-time detection in operational environments. * Real-World Financial Data Utilization and Feature Engineering: The research leverages real-world financial fraud malware data from Bank A in South Korea. Seventy-one features were extracted from eight feature sets, and the accuracy of distinguishing between benign and malicious apps was enhanced by incorporating unique features such as \"User activity\", \"User information\", and \"App package name statistics\", which differentiate this study from previous research. * Extensive ML Model Comparison and Optimization: Multiple machine learning models, including logistic regression, random forest, LightGBM, XGBoost, and CatBoost, were evaluated. Optimized hyperparameters were determined using a grid search to enhance model performance. * Scalable and High-Performance Malware Detection for Financial Institutions: The LightGBM model achieved an accuracy of 99.99% and an F1-score of 97.04%, demonstrating its effectiveness as a scalable, real-time cybersecurity solution for financial institutions. The remainder of this paper is organized as follows: Section 2 provides an overview of related work. Section 3 describes the proposed methodology. Section 4 covers the experiments and results and Section 5 concludes the study.