Artificial intelligence-driven detection of android malware using machine learning techniques
The rapid expansion of Android smartphones and their open-source characteristics have rendered them a primary target for malware attacks, endangering user privacy and device security. With the rise of malware attacks, robust and reliable security is not only wanted but much needed. As the Android ma...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Indonesian Society for Knowledge and Human Development
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46202/1/Artificial%20Intelligence-Driven%20Detection%20of%20Android%20Malware.pdf https://doi.org/10.18517/ijaseit.15.5.13405 https://umpir.ump.edu.my/id/eprint/46202/ |
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| Summary: | The rapid expansion of Android smartphones and their open-source characteristics have rendered them a primary target for malware attacks, endangering user privacy and device security. With the rise of malware attacks, robust and reliable security is not only wanted but much needed. As the Android malware landscape evolves, traditional approaches become increasingly challenging to implement with the same degree of accuracy in detecting new, emerging malware patterns. Recently, machine learning has gained attention as an effective and reliable solution in many application domains, including malware detection. However, traditional methods for detecting malware on Android smartphones are facing new challenges, such as a high false positive rate, performance problems, and a lack of scalability. In this study, we investigate the application of machine learning-based systematic practices to achieve effective and scalable Android malware detection. The experiments were conducted using a dataset consisting of over 15,000 benign and malicious Android apps. A SelectKBest feature selection method was used to reduce computation and improve efficiency by extracting the top 20 most relevant features. The extracted features were then used to train and validate four machine learning classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP). The model was cross-validated using K-fold cross-validation to evaluate its performance on the entire dataset. Random Forest has achieved the highest accuracy among other models, which is 90.64%. The result demonstrates the feasibility of implementing static analysis feature selection and machine learning methods to improve the detection accuracy and computation efficiency. The study provides practical guidance for an optimized static analysis approach that focuses on manifest permissions, enhances detection accuracy, and reduces computational overhead for Android-based malware detection systems, thereby protecting mobile cybersecurity. |
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