Android Malware Classification Based on Fuzzy Hashing Visualization
The proliferation of Android-based devices has brought about an unprecedented surge in mobile application usage, making the Android ecosystem a prime target for cybercriminals. In this paper, a new method for Android malware classification is proposed. The method implements a convolutional neural ne...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/5/4/88 |
| _version_ | 1797380301070008320 |
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| author | Horacio Rodriguez-Bazan Grigori Sidorov Ponciano Jorge Escamilla-Ambrosio |
| author_facet | Horacio Rodriguez-Bazan Grigori Sidorov Ponciano Jorge Escamilla-Ambrosio |
| author_sort | Horacio Rodriguez-Bazan |
| collection | DOAJ |
| description | The proliferation of Android-based devices has brought about an unprecedented surge in mobile application usage, making the Android ecosystem a prime target for cybercriminals. In this paper, a new method for Android malware classification is proposed. The method implements a convolutional neural network for malware classification using images. The research presents a novel approach to transforming the Android Application Package (APK) into a grayscale image. The image creation utilizes natural language processing techniques for text cleaning, extraction, and fuzzy hashing to represent the decompiled code from the APK in a set of hashes after preprocessing, where the image is composed of <i>n</i> fuzzy hashes that represent an APK. The method was tested on an Android malware dataset with 15,493 samples of five malware types. The proposed method showed an increase in accuracy compared to others in the literature, achieving up to 98.24% in the classification task. |
| first_indexed | 2024-03-08T20:35:24Z |
| format | Article |
| id | doaj.art-f455497ed1744a429305bedcafcd9f09 |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-03-08T20:35:24Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-f455497ed1744a429305bedcafcd9f092023-12-22T14:22:13ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902023-11-01541826184710.3390/make5040088Android Malware Classification Based on Fuzzy Hashing VisualizationHoracio Rodriguez-Bazan0Grigori Sidorov1Ponciano Jorge Escamilla-Ambrosio2Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Av. Juan de Dios Batiz, s/n, Mexico City 07320, MexicoCentro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Av. Juan de Dios Batiz, s/n, Mexico City 07320, MexicoCentro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Av. Juan de Dios Batiz, s/n, Mexico City 07320, MexicoThe proliferation of Android-based devices has brought about an unprecedented surge in mobile application usage, making the Android ecosystem a prime target for cybercriminals. In this paper, a new method for Android malware classification is proposed. The method implements a convolutional neural network for malware classification using images. The research presents a novel approach to transforming the Android Application Package (APK) into a grayscale image. The image creation utilizes natural language processing techniques for text cleaning, extraction, and fuzzy hashing to represent the decompiled code from the APK in a set of hashes after preprocessing, where the image is composed of <i>n</i> fuzzy hashes that represent an APK. The method was tested on an Android malware dataset with 15,493 samples of five malware types. The proposed method showed an increase in accuracy compared to others in the literature, achieving up to 98.24% in the classification task.https://www.mdpi.com/2504-4990/5/4/88android malwareconvolutional neural networkdeep learningfuzzy hashingmalware classificationnatural language processing |
| spellingShingle | Horacio Rodriguez-Bazan Grigori Sidorov Ponciano Jorge Escamilla-Ambrosio Android Malware Classification Based on Fuzzy Hashing Visualization Machine Learning and Knowledge Extraction android malware convolutional neural network deep learning fuzzy hashing malware classification natural language processing |
| title | Android Malware Classification Based on Fuzzy Hashing Visualization |
| title_full | Android Malware Classification Based on Fuzzy Hashing Visualization |
| title_fullStr | Android Malware Classification Based on Fuzzy Hashing Visualization |
| title_full_unstemmed | Android Malware Classification Based on Fuzzy Hashing Visualization |
| title_short | Android Malware Classification Based on Fuzzy Hashing Visualization |
| title_sort | android malware classification based on fuzzy hashing visualization |
| topic | android malware convolutional neural network deep learning fuzzy hashing malware classification natural language processing |
| url | https://www.mdpi.com/2504-4990/5/4/88 |
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