Year 2021, Volume 14 , Issue 1, Pages 331 - 356 2021-03-31

Hybroid: A Novel Hybrid Android Malware Detection Framework

Abdullah Talha KABAKUŞ [1]


Android, the most widely-used mobile operating system, attracts the attention of malware developers as well as benign users. Despite the serious proactive actions taken by Android, the Android malware is still widespread as a result of the increasing sophistication and the diversity of malware. Android malware detection systems are generally classified into two: (1) Static analysis, and (2) dynamic analysis. In this study, a novel Android malware detection framework, namely, Hybroid, was proposed which combines both the static and dynamic analysis techniques to benefit from the advantages of both of these techniques. An up-to-date version of Android, namely, Android Oreo, was specifically employed in order to handle the problem from an up-to-date perspective as the recent versions of Android provide new security mechanisms, which are discussed with this study. Hybroid was evaluated on a large dataset that consists of 10,658 applications, and the accuracy of Hybroid was calculated as high as 99.5% when it was utilized with the J48 classification algorithm which outperforms the state-of-the-art studies. The key findings in consequence of the experimental result are discussed in order to shed light on Android malware detection.
Android malware detection, mobile malware, mobile security, static analysis, dynamic analysis, Android
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Primary Language en
Subjects Engineering
Journal Section Makaleler
Authors

Orcid: 0000-0003-2181-4292
Author: Abdullah Talha KABAKUŞ (Primary Author)
Institution: DÜZCE ÜNİVERSİTESİ
Country: Turkey


Dates

Publication Date : March 31, 2021

Bibtex @research article { erzifbed806683, journal = {Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi}, issn = {1307-9085}, eissn = {2149-4584}, address = {}, publisher = {Erzincan University}, year = {2021}, volume = {14}, pages = {331 - 356}, doi = {10.18185/erzifbed.806683}, title = {Hybroid: A Novel Hybrid Android Malware Detection Framework}, key = {cite}, author = {Kabakuş, Abdullah Talha} }
APA Kabakuş, A . (2021). Hybroid: A Novel Hybrid Android Malware Detection Framework . Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi , 14 (1) , 331-356 . DOI: 10.18185/erzifbed.806683
MLA Kabakuş, A . "Hybroid: A Novel Hybrid Android Malware Detection Framework" . Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi 14 (2021 ): 331-356 <https://dergipark.org.tr/en/pub/erzifbed/issue/61106/806683>
Chicago Kabakuş, A . "Hybroid: A Novel Hybrid Android Malware Detection Framework". Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi 14 (2021 ): 331-356
RIS TY - JOUR T1 - Hybroid: A Novel Hybrid Android Malware Detection Framework AU - Abdullah Talha Kabakuş Y1 - 2021 PY - 2021 N1 - doi: 10.18185/erzifbed.806683 DO - 10.18185/erzifbed.806683 T2 - Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi JF - Journal JO - JOR SP - 331 EP - 356 VL - 14 IS - 1 SN - 1307-9085-2149-4584 M3 - doi: 10.18185/erzifbed.806683 UR - https://doi.org/10.18185/erzifbed.806683 Y2 - 2021 ER -
EndNote %0 Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi Hybroid: A Novel Hybrid Android Malware Detection Framework %A Abdullah Talha Kabakuş %T Hybroid: A Novel Hybrid Android Malware Detection Framework %D 2021 %J Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi %P 1307-9085-2149-4584 %V 14 %N 1 %R doi: 10.18185/erzifbed.806683 %U 10.18185/erzifbed.806683
ISNAD Kabakuş, Abdullah Talha . "Hybroid: A Novel Hybrid Android Malware Detection Framework". Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi 14 / 1 (March 2021): 331-356 . https://doi.org/10.18185/erzifbed.806683
AMA Kabakuş A . Hybroid: A Novel Hybrid Android Malware Detection Framework. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2021; 14(1): 331-356.
Vancouver Kabakuş A . Hybroid: A Novel Hybrid Android Malware Detection Framework. Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2021; 14(1): 331-356.
IEEE A. Kabakuş , "Hybroid: A Novel Hybrid Android Malware Detection Framework", Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 14, no. 1, pp. 331-356, Mar. 2021, doi:10.18185/erzifbed.806683