کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6872781 | 1440623 | 2019 | 56 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
AndrODet: An adaptive Android obfuscation detector
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In this paper, we propose AndrODet, a mechanism to detect three popular types of obfuscation in Android applications, namely identifier renaming, string encryption, and control flow obfuscation. AndrODet leverages online learning techniques, thus being suitable for resource-limited environments that need to operate in a continuous manner. We compare our results with a batch learning algorithm using a dataset of 34,962 apps from both malware and benign apps. Experimental results show that online learning approaches are not only able to compete with batch learning methods in terms of accuracy, but they also save significant amount of time and computational resources. Particularly, AndrODet achieves an accuracy of 92.02% for identifier renaming detection, 81.41% for string encryption detection, and 68.32% for control flow obfuscation detection, on average. Also, the overall accuracy of the system when apps might be obfuscated with more than one technique is around 80.66%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 90, January 2019, Pages 240-261
Journal: Future Generation Computer Systems - Volume 90, January 2019, Pages 240-261
نویسندگان
O. Mirzaei, J.M. de Fuentes, J. Tapiador, L. Gonzalez-Manzano,