A machine learning technique for Android malicious attacks detection based on API calls
Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and other malware. Current malware attempts to prevent...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2024-01-01
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| Series: | Decision Science Letters |
| Online Access: | http://www.growingscience.com/dsl/Vol13/dsl_2023_63.pdf |
| _version_ | 1797388900062199808 |
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| author | Mousa AL-Akhras Saud Alghamdi Hani Omar Hazzaa Alshareef |
| author_facet | Mousa AL-Akhras Saud Alghamdi Hani Omar Hazzaa Alshareef |
| author_sort | Mousa AL-Akhras |
| collection | DOAJ |
| description | Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and other malware. Current malware attempts to prevent being detected by any software or anti-virus. This paper describes recent Android malware detection static and interactive approaches as well as several open-source malware datasets. The paper also examines the most current state-of-the-art Android malware identification techniques including identifying by comparative evaluation the gaps between these techniques. As a result, an API-based dynamic malware detection framework is proposed for Android to provide a dynamic paradigm for malware detection. The proposed framework was closely inspected and checked for reliability where meaningful API packages and methods were discovered. |
| first_indexed | 2024-03-08T22:47:38Z |
| format | Article |
| id | doaj.art-babb021c808d42fd8a776eef4eb7c120 |
| institution | Directory Open Access Journal |
| issn | 1929-5804 1929-5812 |
| language | English |
| last_indexed | 2024-03-08T22:47:38Z |
| publishDate | 2024-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | Decision Science Letters |
| spelling | doaj.art-babb021c808d42fd8a776eef4eb7c1202023-12-17T06:28:13ZengGrowing ScienceDecision Science Letters1929-58041929-58122024-01-01131294410.5267/j.dsl.2023.12.004A machine learning technique for Android malicious attacks detection based on API callsMousa AL-AkhrasSaud AlghamdiHani OmarHazzaa Alshareef Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and other malware. Current malware attempts to prevent being detected by any software or anti-virus. This paper describes recent Android malware detection static and interactive approaches as well as several open-source malware datasets. The paper also examines the most current state-of-the-art Android malware identification techniques including identifying by comparative evaluation the gaps between these techniques. As a result, an API-based dynamic malware detection framework is proposed for Android to provide a dynamic paradigm for malware detection. The proposed framework was closely inspected and checked for reliability where meaningful API packages and methods were discovered.http://www.growingscience.com/dsl/Vol13/dsl_2023_63.pdf |
| spellingShingle | Mousa AL-Akhras Saud Alghamdi Hani Omar Hazzaa Alshareef A machine learning technique for Android malicious attacks detection based on API calls Decision Science Letters |
| title | A machine learning technique for Android malicious attacks detection based on API calls |
| title_full | A machine learning technique for Android malicious attacks detection based on API calls |
| title_fullStr | A machine learning technique for Android malicious attacks detection based on API calls |
| title_full_unstemmed | A machine learning technique for Android malicious attacks detection based on API calls |
| title_short | A machine learning technique for Android malicious attacks detection based on API calls |
| title_sort | machine learning technique for android malicious attacks detection based on api calls |
| url | http://www.growingscience.com/dsl/Vol13/dsl_2023_63.pdf |
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