test
server time: root: http://itiis.org
current_path: /journals/tiis/digital-library/21564
current_url: http://itiis.org/journals/tiis/digital-library/21564
Feature Selection to Mine Joint Features from High-dimension Space for Android Malware Detection
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Feature Selection to Mine Joint Features from High-dimension Space for Android Malware Detection


Abstract

Android is now the most popular smartphone platform and remains rapid growth. There are huge number of sensitive privacy information stored in Android devices. Kinds of methods have been proposed to detect Android malicious applications and protect the privacy information. In this work, we focus on extracting the fine-grained features to maximize the information of Android malware detection, and selecting the least joint features to minimize the number of features. Firstly, permissions and APIs, not only from Android permissions and SDK APIs but also from the developer-defined permissions and third-party library APIs, are extracted as features from the decompiled source codes. Secondly, feature selection methods, including information gain (IG), regularization and particle swarm optimization (PSO) algorithms, are used to analyze and utilize the correlation between the features to eliminate the redundant data, reduce the feature dimension and mine the useful joint features. Furthermore, regularization and PSO are integrated to create a new joint feature mining method. Experiment results show that the joint feature mining method can utilize the advantages of regularization and PSO, and ensure good performance and efficiency for Android malware detection.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
Y. Xu, C. Wu, K. Zheng, X. Niu and T. Lu, "Feature Selection to Mine Joint Features from High-dimension Space for Android Malware Detection," KSII Transactions on Internet and Information Systems, vol. 11, no. 9, pp. 4658-4679, 2017. DOI: 10.3837/tiis.2017.09.026.

[ACM Style]
Yanping Xu, Chunhua Wu, Kangfeng Zheng, Xinxin Niu, and Tianling Lu. 2017. Feature Selection to Mine Joint Features from High-dimension Space for Android Malware Detection. KSII Transactions on Internet and Information Systems, 11, 9, (2017), 4658-4679. DOI: 10.3837/tiis.2017.09.026.