Android Malware Detection Using Kullback-Leibler Divergence
Many recent reports suggest that mareware applications cause high billing to victims by sending and receiving hidden SMS messages. Given that, there is a need to develop necessary technique to identify malicious SMS operations as well as differentiate between good and bad SMS operations within appli...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ediciones Universidad de Salamanca
2015-03-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
| Subjects: | |
| Online Access: | https://revistas.usal.es/index.php/2255-2863/article/view/12296 |
| _version_ | 1828837440638418944 |
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| author | Vanessa N. COOPER Hisham M. HADDAD Hossain SHAHRIAR |
| author_facet | Vanessa N. COOPER Hisham M. HADDAD Hossain SHAHRIAR |
| author_sort | Vanessa N. COOPER |
| collection | DOAJ |
| description | Many recent reports suggest that mareware applications cause high billing to victims by sending and receiving hidden SMS messages. Given that, there is a need to develop necessary technique to identify malicious SMS operations as well as differentiate between good and bad SMS operations within applications.<br />In this paper, we apply Kullback-Leibler Divergence (KLD) as a distance metric to identify the difference between good and bad SMS operations. We develop a set of elements that represent sending or receiving of SMS messages, both legitimately and maliciously. Then, we compare the divergence of the trained set of elements. Our evaluation shows that the divergence between good and bad applications remains significantly high, whereas between two applications performing the same SMS operations remain low. We evaluate the proposed KLD-based concept for identifying a set of malware applications. The initial results show that our approach can identify all known malware and has less false positive warning. |
| first_indexed | 2024-12-12T18:42:20Z |
| format | Article |
| id | doaj.art-ee7c2de4e1d14b91b42cf8964a1490c9 |
| institution | Directory Open Access Journal |
| issn | 2255-2863 |
| language | English |
| last_indexed | 2024-12-12T18:42:20Z |
| publishDate | 2015-03-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj.art-ee7c2de4e1d14b91b42cf8964a1490c92022-12-22T00:15:37ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632015-03-0132172510.14201/ADCAIJ201432172511501Android Malware Detection Using Kullback-Leibler DivergenceVanessa N. COOPER0Hisham M. HADDAD1Hossain SHAHRIAR2Department of Computer Science, Kennesaw State University, Kennesaw, Georgia, USADepartment of Computer Science, Kennesaw State University, Kennesaw, Georgia, USADepartment of Computer Science, Kennesaw State University, Kennesaw, Georgia, USAMany recent reports suggest that mareware applications cause high billing to victims by sending and receiving hidden SMS messages. Given that, there is a need to develop necessary technique to identify malicious SMS operations as well as differentiate between good and bad SMS operations within applications.<br />In this paper, we apply Kullback-Leibler Divergence (KLD) as a distance metric to identify the difference between good and bad SMS operations. We develop a set of elements that represent sending or receiving of SMS messages, both legitimately and maliciously. Then, we compare the divergence of the trained set of elements. Our evaluation shows that the divergence between good and bad applications remains significantly high, whereas between two applications performing the same SMS operations remain low. We evaluate the proposed KLD-based concept for identifying a set of malware applications. The initial results show that our approach can identify all known malware and has less false positive warning.https://revistas.usal.es/index.php/2255-2863/article/view/12296android malware detectionkullback-leibler divergenceback-off smoothing |
| spellingShingle | Vanessa N. COOPER Hisham M. HADDAD Hossain SHAHRIAR Android Malware Detection Using Kullback-Leibler Divergence Advances in Distributed Computing and Artificial Intelligence Journal android malware detection kullback-leibler divergence back-off smoothing |
| title | Android Malware Detection Using Kullback-Leibler Divergence |
| title_full | Android Malware Detection Using Kullback-Leibler Divergence |
| title_fullStr | Android Malware Detection Using Kullback-Leibler Divergence |
| title_full_unstemmed | Android Malware Detection Using Kullback-Leibler Divergence |
| title_short | Android Malware Detection Using Kullback-Leibler Divergence |
| title_sort | android malware detection using kullback leibler divergence |
| topic | android malware detection kullback-leibler divergence back-off smoothing |
| url | https://revistas.usal.es/index.php/2255-2863/article/view/12296 |
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