![](/images/graphics-bg.png)
Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion
المؤلفون المشاركون
Zhang, Dafang
Su, Xin
Wang, Xin
Li, Wenjia
المصدر
Security and Communication Networks
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-08-28
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
In recent years, Android malware has continued to grow at an alarming rate.
More recent malicious apps’ employing highly sophisticated detection avoidance techniques makes the traditional machine learning based malware detection methods far less effective.
More specifically, they cannot cope with various types of Android malware and have limitation in detection by utilizing a single classification algorithm.
To address this limitation, we propose a novel approach in this paper that leverages parallel machine learning and information fusion techniques for better Android malware detection, which is named Mlifdect.
To implement this approach, we first extract eight types of features from static analysis on Android apps and build two kinds of feature sets after feature selection.
Then, a parallel machine learning detection model is developed for speeding up the process of classification.
Finally, we investigate the probability analysis based and Dempster-Shafer theory based information fusion approaches which can effectively obtain the detection results.
To validate our method, other state-of-the-art detection works are selected for comparison with real-world Android apps.
The experimental results demonstrate that Mlifdect is capable of achieving higher detection accuracy as well as a remarkable run-time efficiency compared to the existing malware detection solutions.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Xin& Zhang, Dafang& Su, Xin& Li, Wenjia. 2017. Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion. Security and Communication Networks،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1203054
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Xin…[et al.]. Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion. Security and Communication Networks No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1203054
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Xin& Zhang, Dafang& Su, Xin& Li, Wenjia. Mlifdect: Android Malware Detection Based on Parallel Machine Learning and Information Fusion. Security and Communication Networks. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1203054
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1203054
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)