Android Malware Family Classification Based On Deep Learning |
Author(s): |
| Himanshu Maurya , SRM Institute of Science and Technology, Ramapuram, India; Pradeep N, SRM Institute of Science and Technology, Ramapuram, India; Tejas Srivastava, SRM Institute of Science and Technology, Ramapuram, India; Priya M, SRM Institute of Science and Technology, Ramapuram, India |
Keywords: |
| Android Malware; Malware Family Bracket; Machine Literacy; Erected- In Authorization; Custom Authorization; Balanced Delicacy |
Abstract |
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To handle relentlessly arising Android malware, deep literacy has been extensively espoused in the exploration community. Previous work has proposed insight approaches that exploit various characteristics of malware and reported a high level of sophistication in malware detection, i.e the classification of malware according to benign operations. Still, domestic analysis of real- world Android malware has not been considerably studied yet. Familial analysis refers to the process of classifying a given malware into a family (set of families), which can greatly accelerate malware analysis as the analysis gives there, we gain behavioral characteristics. In this article, we analyze by looking at the different characteristics of Android malware and how effectively they can display their (erroneous) behavior. |
Other Details |
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Paper ID: IJSRDV11I30077 Published in: Volume : 11, Issue : 3 Publication Date: 01/06/2023 Page(s): 136-142 |
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