Enhanced android malware detection and family classification, using conversation-level network traffic features
Joint Authors
Abu thawapih, Muhammad
Mahmud, Khalid
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 4A (s) (31 Jul. 2020), pp.607-614, 8 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-07-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
Signature-based malware detection algorithms are facing challenges to cope with the massive number of threats in the Android environment.
In this paper, conversation-level network traffic features are extracted and used in a supervised- based model.
This model was used to enhance the process of Android malware detection, categorization, and family classification.
The model employs the ensemble learning technique in order to select the most useful features among the extracted features.
A real-world dataset called CICAndMal2017 was used in this paper.
The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75%, 79.97%, and 66.71%for malware detection, malware categorization, and malware family classification respectively.
A comparison with another study that uses the same dataset was made.
This study has achieved a significant enhancement in malware family classification and malware categorization.
For malware family classification, the enhancement was 39.71% for precision and 41.09% for recall.
The rate of enhancement for the Android malware categorization was 30.2% and 31.14% for precision and recall, respectively.
American Psychological Association (APA)
Abu thawapih, Muhammad& Mahmud, Khalid. 2020. Enhanced android malware detection and family classification, using conversation-level network traffic features. The International Arab Journal of Information Technology،Vol. 17, no. 4A (s), pp.607-614.
https://search.emarefa.net/detail/BIM-1432241
Modern Language Association (MLA)
Abu thawapih, Muhammad& Mahmud, Khalid. Enhanced android malware detection and family classification, using conversation-level network traffic features. The International Arab Journal of Information Technology Vol. 17, no. 4A (Special issue) (2020), pp.607-614.
https://search.emarefa.net/detail/BIM-1432241
American Medical Association (AMA)
Abu thawapih, Muhammad& Mahmud, Khalid. Enhanced android malware detection and family classification, using conversation-level network traffic features. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 4A (s), pp.607-614.
https://search.emarefa.net/detail/BIM-1432241
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 612-614
Record ID
BIM-1432241