Comparative Analysis of Machine Learning Models for Android Malware Detection
The rapid growth of Android devices has led to increased security concerns, especially from malicious software. This study extensively compares machine-learning algorithms for effective Android malware detection. Traditional models, such as random forest (RF) and support vector machines (SVM), along...
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| Format: | Article |
| Language: | English |
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Sakarya University
2024-06-01
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| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/3366215 |
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| author | Adem Korkmaz Selma Bulut |
| author_facet | Adem Korkmaz Selma Bulut |
| author_sort | Adem Korkmaz |
| collection | DOAJ |
| description | The rapid growth of Android devices has led to increased security concerns, especially from malicious software. This study extensively compares machine-learning algorithms for effective Android malware detection. Traditional models, such as random forest (RF) and support vector machines (SVM), alongside advanced approaches, such as convolutional neural networks (CNN) and XGBoost, were evaluated. Leveraging the NATICUSdroid dataset containing 29,332 records and 86 traces, the results highlight the superiority of RF with 97.1% and XGBoost with 97.2% accuracy. However, evolving malware and real-world unpredictability require a cautious interpretation. Promising as they are, our findings stress the need for continuous innovation in malware detection to ensure robust Android user security and data integrity. |
| format | Article |
| id | doaj-art-e843eeec56de4d46b8b25fa9d41c70b1 |
| institution | DOAJ |
| issn | 2147-835X |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| spelling | doaj-art-e843eeec56de4d46b8b25fa9d41c70b12025-08-20T02:40:23ZengSakarya UniversitySakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi2147-835X2024-06-0128351753010.16984/saufenbilder.135083928Comparative Analysis of Machine Learning Models for Android Malware DetectionAdem Korkmaz0https://orcid.org/0000-0002-7530-7715Selma Bulut1https://orcid.org/0000-0002-6559-7704BANDIRMA ONYEDI EYLUL UNIVERSITY, GÖNEN VOCATIONAL SCHOOLKIRKLARELI UNIVERSITY, TECHNICAL SCIENCES VOCATIONAL SCHOOLThe rapid growth of Android devices has led to increased security concerns, especially from malicious software. This study extensively compares machine-learning algorithms for effective Android malware detection. Traditional models, such as random forest (RF) and support vector machines (SVM), alongside advanced approaches, such as convolutional neural networks (CNN) and XGBoost, were evaluated. Leveraging the NATICUSdroid dataset containing 29,332 records and 86 traces, the results highlight the superiority of RF with 97.1% and XGBoost with 97.2% accuracy. However, evolving malware and real-world unpredictability require a cautious interpretation. Promising as they are, our findings stress the need for continuous innovation in malware detection to ensure robust Android user security and data integrity.https://dergipark.org.tr/tr/download/article-file/3366215android malware detectionmachine learning algorithmsnaticusdroid datasetcomparative analysisdata integrity |
| spellingShingle | Adem Korkmaz Selma Bulut Comparative Analysis of Machine Learning Models for Android Malware Detection Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi android malware detection machine learning algorithms naticusdroid dataset comparative analysis data integrity |
| title | Comparative Analysis of Machine Learning Models for Android Malware Detection |
| title_full | Comparative Analysis of Machine Learning Models for Android Malware Detection |
| title_fullStr | Comparative Analysis of Machine Learning Models for Android Malware Detection |
| title_full_unstemmed | Comparative Analysis of Machine Learning Models for Android Malware Detection |
| title_short | Comparative Analysis of Machine Learning Models for Android Malware Detection |
| title_sort | comparative analysis of machine learning models for android malware detection |
| topic | android malware detection machine learning algorithms naticusdroid dataset comparative analysis data integrity |
| url | https://dergipark.org.tr/tr/download/article-file/3366215 |
| work_keys_str_mv | AT ademkorkmaz comparativeanalysisofmachinelearningmodelsforandroidmalwaredetection AT selmabulut comparativeanalysisofmachinelearningmodelsforandroidmalwaredetection |