TinyDroid: A Lightweight and Efficient Model for Android Malware Detection and Classification
Joint Authors
Chen, Tieming
Lv, MingQi
Mao, Qingyu
Yang, Yimin
Zhu, Jianming
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-17
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Telecommunications Engineering
Abstract EN
With the popularity of Android applications, Android malware has an exponential growth trend.
In order to detect Android malware effectively, this paper proposes a novel lightweight static detection model, TinyDroid, using instruction simplification and machine learning technique.
First, a symbol-based simplification method is proposed to abstract the opcode sequence decompiled from Android Dalvik Executable files.
Then, N-gram is employed to extract features from the simplified opcode sequence, and a classifier is trained for the malware detection and classification tasks.
To improve the efficiency and scalability of the proposed detection model, a compression procedure is also used to reduce features and select exemplars for the malware sample dataset.
TinyDroid is compared against the state-of-the-art antivirus tools in real world using Drebin dataset.
The experimental results show that TinyDroid can get a higher accuracy rate and lower false alarm rate with satisfied efficiency.
American Psychological Association (APA)
Chen, Tieming& Mao, Qingyu& Yang, Yimin& Lv, MingQi& Zhu, Jianming. 2018. TinyDroid: A Lightweight and Efficient Model for Android Malware Detection and Classification. Mobile Information Systems،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1204782
Modern Language Association (MLA)
Chen, Tieming…[et al.]. TinyDroid: A Lightweight and Efficient Model for Android Malware Detection and Classification. Mobile Information Systems No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1204782
American Medical Association (AMA)
Chen, Tieming& Mao, Qingyu& Yang, Yimin& Lv, MingQi& Zhu, Jianming. TinyDroid: A Lightweight and Efficient Model for Android Malware Detection and Classification. Mobile Information Systems. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1204782
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1204782