Comparative Analysis of Ensemble Methods for Classification of Android Malicious Applications
作者： Meghna Dhalaria , Ekta Gandotra , Suman Saha
DOI: 10.1007/978-981-13-9939-8_33
关键词: Support vector machine 、 Android (operating system) 、 Machine learning 、 Naive Bayes classifier 、 Boosting (machine learning) 、 Random forest 、 Decision tree 、 Artificial intelligence 、 Ensemble learning 、 Computer science 、 Application programming interface
摘要: Currently, Android smartphone operating systems are the most popular entity found in market. It is open source software which allows developers to take complete benefit of mobile operation device, but additionally increases sizable issues related malicious applications. With increase phone users, risk malware increasing. This paper compares basic machine learning algorithms and different ensemble methods for classifying Various such as Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree Naive Bayes like Bagging, Boosting Stacking applied on a dataset comprising permissions, intents, Application programming interface (API) calls command signatures extracted from The results revealed that stacking techniques performed better compared base classifiers.
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sci-hub.se 参考文章(20)
Borja Sanz, Igor Santos, Carlos Laorden, Xabier Ugarte-Pedrero, Pablo Garcia Bringas, Gonzalo Álvarez, PUMA: Permission Usage to Detect Malware in Android CISIS/ICEUTE/SOCO Special Sessions. pp. 289- 298 ,(2013) , 10.1007/978-3-642-33018-6_30
Matthew Wiener, Andy Liaw, Classification and Regression by randomForest ,(2007)
K. M. Ting, I. H. Witten, Issues in stacked generalization Journal of Artificial Intelligence Research. ,vol. 10, pp. 271- 289 ,(1999) , 10.1613/JAIR.594
Martina Lindorfer, Matthias Neugschwandtner, Christian Platzer, None, MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis computer software and applications conference. ,vol. 2, pp. 422- 433 ,(2015) , 10.1109/COMPSAC.2015.103
Chun-Ying Huang, Yi-Ting Tsai, Chung-Han Hsu, Performance Evaluation on Permission-Based Detection for Android Malware Advances in Intelligent Systems and Applications - Volume 2. pp. 111- 120 ,(2013) , 10.1007/978-3-642-35473-1_12
Borja Sanz, Igor Santos, Carlos Laorden, Xabier Ugarte-Pedrero, Javier Nieves, Pablo G Bringas, Gonzalo Álvarez Marañón, None, MAMA: MANIFEST ANALYSIS FOR MALWARE DETECTION IN ANDROID Cybernetics and Systems. ,vol. 44, pp. 469- 488 ,(2013) , 10.1080/01969722.2013.803889
Shweta Bhandari, Rishabh Gupta, Vijay Laxmi, Manoj Singh Gaur, Akka Zemmari, Maxim Anikeev, DRACO: DRoid analyst combo an android malware analysis framework security of information and networks. pp. 283- 289 ,(2015) , 10.1145/2799979.2800003
Lei Cen, Christoher S. Gates, Luo Si, Ninghui Li, A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code IEEE Transactions on Dependable and Secure Computing. ,vol. 12, pp. 400- 412 ,(2015) , 10.1109/TDSC.2014.2355839
Ekta Gandotra, Divya Bansal, Sanjeev Sofat, Malware Analysis and Classification: A Survey Journal of Information Security. ,vol. 5, pp. 56- 64 ,(2014) , 10.4236/JIS.2014.52006
Anshul Arora, Shree Garg, Sateesh K. Peddoju, Malware Detection Using Network Traffic Analysis in Android Based Mobile Devices next generation mobile applications, services and technologies. pp. 66- 71 ,(2014) , 10.1109/NGMAST.2014.57