Key features for the characterization of Android malware families

In recent years, mobile devices such as smartphones, tablets and wearables have become the new paradigm of user–computer interaction. The increasing use and adoption of such devices is also leading to an increased number of potential security risks. The spread of mobile malware, particularly on popular and open platforms such as Android, has become a major concern. This paper focuses on the bad-intentioned Android apps by addressing the problem of selecting the key features of such software that support the characterization of such malware. The accurate detection and characterization of this software is still an open challenge, mainly due to its ever-changing nature and the open distribution channels of Android apps. Maximum relevance minimum redundancy and evolutionary algorithms guided by information correlation measures have been applied for feature selection on the well-known Android Malware Genome (Malgenome) dataset, attaining interesting results on the most informative features for the characterization of representative families of existing Android malware.

[1]  Álvaro Herrero,et al.  On the Selection of Key Features for Android Malware Characterization , 2015, CISIS-ICEUTE.

[2]  Saba Arshad,et al.  Android Malware Detection & Protection: A Survey , 2016 .

[3]  Chris H. Q. Ding,et al.  Minimum redundancy feature selection from microarray gene expression data , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[4]  Simin Nadjm-Tehrani,et al.  Detection and Visualization of Android Malware Behavior , 2016, J. Electr. Comput. Eng..

[5]  Yajin Zhou,et al.  Dissecting Android Malware: Characterization and Evolution , 2012, 2012 IEEE Symposium on Security and Privacy.

[6]  Yuval Elovici,et al.  “Andromaly”: a behavioral malware detection framework for android devices , 2012, Journal of Intelligent Information Systems.

[7]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[8]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[9]  Camelia Chira,et al.  Diverse accurate feature selection for microarray cancer diagnosis , 2013, Intell. Data Anal..

[10]  Stefan Kraxberger,et al.  Malware detection by applying knowledge discovery processes to application metadata on the Android Market (Google Play) , 2016, Secur. Commun. Networks.

[11]  Concha Bielza,et al.  Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.

[12]  Gonzalo Álvarez,et al.  MAMA: MANIFEST ANALYSIS FOR MALWARE DETECTION IN ANDROID , 2013, Cybern. Syst..

[13]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[14]  Lei Liu,et al.  Ensemble gene selection by grouping for microarray data classification , 2010, J. Biomed. Informatics.

[15]  Madhumita Chatterjee,et al.  A Novel Approach to Detect Android Malware , 2015 .

[16]  Razvan Deaconescu,et al.  Smart malware detection on Android , 2015, Secur. Commun. Networks.

[17]  Pengcheng Ma,et al.  ψ-Contraction and $$(\phi ,\varphi )$$(ϕ,φ)-contraction in Menger probabilistic metric space , 2016, SpringerPlus.

[18]  Ainuddin Wahid Abdul Wahab,et al.  A review on feature selection in mobile malware detection , 2015, Digit. Investig..

[19]  Luo Si,et al.  A Probabilistic Discriminative Model for Android Malware Detection with Decompiled Source Code , 2015, IEEE Transactions on Dependable and Secure Computing.

[20]  Sohail Asghar,et al.  A REVIEW OF FEATURE SELECTION TECHNIQUES IN STRUCTURE LEARNING , 2013 .

[21]  Vijay Laxmi,et al.  MCF: MultiComponent Features for Malware Analysis , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[22]  Jane Labadin,et al.  Feature selection based on mutual information , 2015, 2015 9th International Conference on IT in Asia (CITA).

[23]  Aziz Mohaisen,et al.  Detecting and classifying method based on similarity matching of Android malware behavior with profile , 2016, SpringerPlus.