استفاده از دستگاه های واقعی برای تشخیص بدافزار اندرویدی
ترجمه نشده

استفاده از دستگاه های واقعی برای تشخیص بدافزار اندرویدی

عنوان فارسی مقاله: DL-ربات انسان نما: تشخیص بدافزار اندرویدی مبتنی بر یادگیری عمیق با استفاده از دستگاه های واقعی
عنوان انگلیسی مقاله: DL-Droid: Deep learning based android malware detection using real devices
مجله/کنفرانس: رایانه ها و امنیت - Computers & Security
رشته های تحصیلی مرتبط: کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی نرم افزار، طراحی و تولید نرم افزار، برنامه نویسی کامپیوتر، امنیت اطلاعات
کلمات کلیدی فارسی: اندروید، پوشش کد، یادگیری عمیق، آنالیز پویا، یادگیری ماشین، شناسایی بدافزار، امنیت موبایل، تجزیه و تحلیل استاتیک
کلمات کلیدی انگلیسی: Android، Code coverage، Deep learning، Dynamic analysis، Machine learning، Malware detection، Mobile security، Static analysis
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.cose.2019.101663
دانشگاه: College of Computing in Al-Qunfudah, Umm Al-Qura University, Saudi Arabia
صفحات مقاله انگلیسی: 11
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 4/337 در سال 2019
شاخص H_index: 77 در سال 2020
شاخص SJR: 0/667 در سال 2019
شناسه ISSN: 0167-4048
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14424
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Related work

3- Methodology and experiments

4- Experimental results and discussions

5- Conclusion

References

بخشی از مقاله (انگلیسی)

Abstract

The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches.

Introduction

Android operating system, which is provided by Google, is predicted to continue have a dramatic increase in the market with around 1.5 billion Android-based devices to be shipped by 2021 sta. It is currently leading the mobile OS market with over 80% market share compared to iOS, Windows, Blackberry, and Symbian OS. The availability of diverse Android markets such as Google Play, the official store, and third-party markets makes Android devices a popular target to not only legitimate developers, but also malware developers. Over one billion devices have been sold and more than 65 billion downloads have been made from Google Play (Smartphone, 0000). Android apps can be found in different categories, such as educational apps, gaming apps, social media apps, entertainment apps, banking apps, etc. As a technology that is open source and widely adopted, Android is facing many challenges especially with malicious applications. The malware infected apps have the ability to send text mes-sages to premium rate numbers without the user acknowledgment, gain access to private data, or even install code that can download and execute additional malware on the victim’s device. The malware can also be used to create mobile botnets (Anagnostopoulos et al., 2016). Over the last few years, the number of malware samples attacking Android has significantly increased. According to a recent report from McAfee, over 2.5 million new Android malware apps were discovered in 2017, thus increasing the number of mobile malware samples in the wild to almost 25 million in 2017 (McA, 0000).