Apk2Audio4AndMal: Audio Based Malware Family Detection Framework
Due to Android’s popularity, cybercriminals view it as a lucrative target. Malwares with varying behavior patterns that specifically target user routines are constantly entering the market. Because of this, knowing how to identify different forms of malware is crucial for protecting again...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10073518/ |
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| author | Oguz Emre Kural Erdal Kilic Ceyda Aksac |
| author_facet | Oguz Emre Kural Erdal Kilic Ceyda Aksac |
| author_sort | Oguz Emre Kural |
| collection | DOAJ |
| description | Due to Android’s popularity, cybercriminals view it as a lucrative target. Malwares with varying behavior patterns that specifically target user routines are constantly entering the market. Because of this, knowing how to identify different forms of malware is crucial for protecting against it. This paper proposes an audio-based malware family detection approach to achieve this goal. Android applications were converted to audio files in.wav format, and their audio-based features were extracted. Then, CFS-Subset, ReliefF, Information Gain, and Gain Ratio feature selection methods were applied to the extracted features. By examining the subsets obtained, features with high discrimination in Android malware family detection were determined. Classification experiments were conducted with the dataset created by randomly selected 500 samples from 8 families in AMD and Drebin datasets. Experiments with five different classifiers showed that effective malware family classification could be made with a small number of features in the audio domain. |
| format | Article |
| id | doaj-art-0c5d685828f940eab7e0c36bea472886 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0c5d685828f940eab7e0c36bea4728862025-02-08T00:00:12ZengIEEEIEEE Access2169-35362023-01-0111275272753510.1109/ACCESS.2023.325837710073518Apk2Audio4AndMal: Audio Based Malware Family Detection FrameworkOguz Emre Kural0https://orcid.org/0000-0002-8406-4823Erdal Kilic1Ceyda Aksac2https://orcid.org/0000-0003-0022-789XDepartment of Computer Engineering, Ondokuz May¹s University, Samsun, TurkeyDepartment of Computer Engineering, Ondokuz May¹s University, Samsun, TurkeyRönesans Holding, Ankara, TurkeyDue to Android’s popularity, cybercriminals view it as a lucrative target. Malwares with varying behavior patterns that specifically target user routines are constantly entering the market. Because of this, knowing how to identify different forms of malware is crucial for protecting against it. This paper proposes an audio-based malware family detection approach to achieve this goal. Android applications were converted to audio files in.wav format, and their audio-based features were extracted. Then, CFS-Subset, ReliefF, Information Gain, and Gain Ratio feature selection methods were applied to the extracted features. By examining the subsets obtained, features with high discrimination in Android malware family detection were determined. Classification experiments were conducted with the dataset created by randomly selected 500 samples from 8 families in AMD and Drebin datasets. Experiments with five different classifiers showed that effective malware family classification could be made with a small number of features in the audio domain.https://ieeexplore.ieee.org/document/10073518/Androidmalware detectionfamily classificationaudio basedfeature selectionmachine learning |
| spellingShingle | Oguz Emre Kural Erdal Kilic Ceyda Aksac Apk2Audio4AndMal: Audio Based Malware Family Detection Framework IEEE Access Android malware detection family classification audio based feature selection machine learning |
| title | Apk2Audio4AndMal: Audio Based Malware Family Detection Framework |
| title_full | Apk2Audio4AndMal: Audio Based Malware Family Detection Framework |
| title_fullStr | Apk2Audio4AndMal: Audio Based Malware Family Detection Framework |
| title_full_unstemmed | Apk2Audio4AndMal: Audio Based Malware Family Detection Framework |
| title_short | Apk2Audio4AndMal: Audio Based Malware Family Detection Framework |
| title_sort | apk2audio4andmal audio based malware family detection framework |
| topic | Android malware detection family classification audio based feature selection machine learning |
| url | https://ieeexplore.ieee.org/document/10073518/ |
| work_keys_str_mv | AT oguzemrekural apk2audio4andmalaudiobasedmalwarefamilydetectionframework AT erdalkilic apk2audio4andmalaudiobasedmalwarefamilydetectionframework AT ceydaaksac apk2audio4andmalaudiobasedmalwarefamilydetectionframework |