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Automated Android Malware Detection
The rapid growth of smartphone usage, driven by affordability and the digitalization of services, has introduced significant security challenges. Among these, malware threats have become a major concern, particularly on the Android platform, where the proliferation of malicious and fraudulent applications has increased substantially. As Android devices continue to gain popularity, malware developers consistently create new threats, compromising system integrity and user privacy. This study aims to apply Machine Learning (ML) techniques for effective Android malware detection. A comprehensive detection framework is proposed, incorporating six ML models for classifying different types of malware, including Decision Trees, Support Vector Machines (SVM), Naive Bayes, Random Forests, K-Nearest Neighbors (KNN), and Ensemble Methods such as the Extra Trees Classifier. The framework is evaluated using the CICMalAnal2017 dataset, which includes diverse malware categories such as adware, ransomware, and scareware. To enhance model performance, multiple feature selection techniques are employed, including Feature Correlation, Random Forest Importance, Chi-Square Test, and Information Gain. These methods help identify the most relevant features for accurate classification. The performance of various ML algorithms is analyzed and compared to determine the most effective approach for malware detection. Furthermore, the study highlights the importance of using ML-based techniques to detect vulnerabilities at the source code level, as implementing security measures after application deployment can be more challenging. Overall, this research contributes to a deeper understanding of Android malware detection and provides insights into potential future directions for improving mobile security systems.
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