An ensemble-based supervised machine learning framework for android ransomware detection
Joint Authors
Sharma, Shweta
Challa, Rama Krishna
Kumar, Rakesh
Source
The International Arab Journal of Information Technology
Issue
Vol. 18, Issue 3A (s) (31 May. 2021), pp.422-429, 8 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2021-05-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
With latest development in technology, the usage of smartphones to fulfill day-to-day requirements has been increased.
The Android-based smartphones occupy the largest market share among other mobile operating systems.
The hackers are continuously keeping an eye on Android-based smartphones by creating malicious apps housed with ransomware functionality for monetary purposes.
Hackers lock the screen and/or encrypt the documents of the victim’s Android based smartphones after performing ransomware attacks.
Thus, in this paper, a framework has been proposed in which we (1) utilize novel features of Android ransomware, (2) reduce the dimensionality of the features, (3) employ an ensemble learning model to detect Android ransomware, and (4) perform a comparative analysis to calculate the computational time required by machine learning models to detect Android ransomware.
Our proposed framework can efficiently detect both locker and crypto ransomware.
The experimental results reveal that the proposed framework detects Android ransomware by achieving an accuracy of 99.67% with Random Forest ensemble model.
After reducing the dimensionality of the features with principal component analysis technique; the Logistic Regression model took least time to execute on the Graphics Processing Unit (GPU) and Central Processing Unit (CPU) in 41 milliseconds and 50 milliseconds respectively.
American Psychological Association (APA)
Sharma, Shweta& Challa, Rama Krishna& Kumar, Rakesh. 2021. An ensemble-based supervised machine learning framework for android ransomware detection. The International Arab Journal of Information Technology،Vol. 18, no. 3A (s), pp.422-429.
https://search.emarefa.net/detail/BIM-1439914
Modern Language Association (MLA)
Sharma, Shweta…[et al.]. An ensemble-based supervised machine learning framework for android ransomware detection. The International Arab Journal of Information Technology Vol. 18, no. 3A (Special issue) (2021), pp.422-429.
https://search.emarefa.net/detail/BIM-1439914
American Medical Association (AMA)
Sharma, Shweta& Challa, Rama Krishna& Kumar, Rakesh. An ensemble-based supervised machine learning framework for android ransomware detection. The International Arab Journal of Information Technology. 2021. Vol. 18, no. 3A (s), pp.422-429.
https://search.emarefa.net/detail/BIM-1439914
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 428-429
Record ID
BIM-1439914