一种使用静态分析和遗传搜索在Android恶意软件检测中搜索最优特征的方法

Ahmad FIRDAUS; Nor Badrul ANUAR; Ahmad KARIM; Mohd Faizal Ab RAZAK

doi:10.1631/FITEE.1601491

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一种使用静态分析和遗传搜索在Android恶意软件检测中搜索最优特征的方法

常规文章 | Updated：2022-05-19

- 一种使用静态分析和遗传搜索在Android恶意软件检测中搜索最优特征的方法
- Discovering optimal features using static analysis and a genetic search based method for Android malware detection
- 信息与电子工程前沿（英文） 2018年19卷第6期页码：712-736
- Affiliations：
  
  Department of Computer System and Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
  Faculty of Computer System & Software Engineering, University Malaysia Pahang, Gambang 26300, Malaysia
  Department of Information Technology, Bahauddin Zakariya University, Multan 60000, Pakistan
- Author bio：
  
  Ahmad FIRDAUS, E-mail: ahmadfirdaus@um.edu.my
  Nor Badrul ANUAR, E-mail: badrul@um.edu.my
- Funds：
  
  Project supported by the Ministry of Science, Technology and Innovation of Malaysia, under the Grant eScienceFund (No. 01-01-03- SF0914)
- DOI：10.1631/FITEE.1601491
  中图分类号： TP309.5
- 收稿：2016-08-22，
  
  修回：2018-;6-;8，
  
  纸质出版：2018-06
- Accepted：
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Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, 等. 一种使用静态分析和遗传搜索在Android恶意软件检测中搜索最优特征的方法[J]. 信息与电子工程前沿（英文）, 2018,19(6):712-736.

Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, et al. Discovering optimal features using static analysis and a genetic search based method for Android malware detection[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(6): 712-736.
Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, 等. 一种使用静态分析和遗传搜索在Android恶意软件检测中搜索最优特征的方法[J]. 信息与电子工程前沿（英文）, 2018,19(6):712-736. DOI： 10.1631/FITEE.1601491.

Ahmad FIRDAUS, Nor Badrul ANUAR, Ahmad KARIM, et al. Discovering optimal features using static analysis and a genetic search based method for Android malware detection[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(6): 712-736. DOI： 10.1631/FITEE.1601491.

摘要

移动设备制造商在全球范围内快速开发各种Android版本。同时，网络罪犯也在实施各种恶意行为，例如跟踪用户活动、窃取个人数据以及实施银行诈骗。由于在日常生活中使用Android进行重要通信的人群数量庞大，这些网络罪犯从中获得了巨大非法收益。为此，安全从业者通过静态和动态分析对恶意软件进行识别。静态分析具有整体代码覆盖、低资源消耗和快速处理的优势。然而，静态分析需要最少量的特征才能对恶意软件进行有效分类。因此，我们采用基于遗传算法（GA）的遗传搜索（GS）在106个字符串中选择特征。为评估由GS确定的最佳特征，我们使用了5种机器学习分类器，分别是Naïve Bayes（NB）、功能树（FT）、J48、随机森林（RF）和多层感知器（MLP）。在这5种分类器中，FT仅使用6种特征，获得最高准确度（95％）和最高真正率（TPR）（96.7％）。

Abstract

Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously

cyber criminals are executing malicious actions

such as tracking user activities

stealing personal data

and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines

including important communications. With this in mind

security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage

low resource consumption

and rapid processing. However

static analysis requires a minimum number of features to efficiently classify malware. Therefore

we used genetic search (GS)

which is a search based on a genetic algorithm (GA)

to select the features among 106 strings. To evaluate the best features determined by GS

we used five machine learning classifiers

namely

Naïve Bayes (NB)

functional trees (FT)

J48

random forest (RF)

and multilayer perceptron (MLP). Among these classifiers

FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use of only six features.

关键词

Keywords

references

Y Aafer , , , WL Du , , , H Yin . . Droidapiminer: mining API-level features for robust malware detection in Android . . Proc 9 th Int ICST Conf on Security and Privacy in Communication Networks , , 2013 . . p.86 - - 103 . . . .

KS Adewole , , , NB Anuar , , , A Kamsin , , , 等 . . Malicious accounts: dark of the social networks . . J Netw Comput Appl , , 2017 . . 79 41 - - 67 . . DOI: 10.1016/j.jnca.2016.11.030 http://doi.org/10.1016/j.jnca.2016.11.030 . .

F Afifi , , , NB Anuar , , , S Shamshirband , , , 等 . . Dyhap: dynamic hybrid ANFIS-PSO approach for predicting mobile malware . . PLoS ONE , , 2016 . . 11 ( ( 9 ): ): e0162627 DOI: 10.1371/journal.pone.0162627 http://doi.org/10.1371/journal.pone.0162627 . .

Android . . App manifest . . http://developer.Android.com/guide/topics/manifest/manifest-intro.html http://developer.Android.com/guide/topics/manifest/manifest-intro.html [Accessed on Apr. 28] , , 2015 . . 2015 . .

Developers Android . . Android security overview . . Android. https://source.Android.com/devices/tech/security/ https://source.Android.com/devices/tech/security/ [Accessed on Sept. 1] , , 2015 . . 2015 . .

NB Anuar , , , H Sallehudin , , , A Gani , , , 等 . . Identifying false alarm for network intrusion detection system using hybrid data mining and decision tree . . Malays J Comput Sci , , 2008 . . 21 ( ( 2 ): ): 101 - - 115 . . . .

NB Anuar , , , M Papadaki , , , S Furnell , , , 等 . . Incident prioritisation using analytic hierarchy process (AHP): risk index model (RIM) . . Secur Commun Netw , , 2013 . . 6 ( ( 9 ): ): 1087 - - 1116 . . DOI: 10.1002/sec.673 http://doi.org/10.1002/sec.673 . .

A Apvrille , , , T Strazzere . . Reducing the window of opportunity for Android malware gotta catch 'em all . . J Comput Virol , , 2012 . . 8 ( ( 1-2 ): ): 61 - - 71 . . DOI: 10.1007/s11416-012-0162-3 http://doi.org/10.1007/s11416-012-0162-3 . .

D Arp , , , M Spreitzenbarth , , , H Malte , , , 等 . . Drebin: effective and explainable detection of Android malware in your pocket . . Proc Symp on Network and Distributed System Security , , 2014 . . p.1 - - 15 . . . .

S Arzt , , , S Rasthofer , , , C Fritz , , , 等 . . Flowdroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android Apps . . Proc 35 th ACM SIGPLAN Conf on Programming Language Design and Implementation , , 2014 . . p.259 - - 269 . . DOI: 10.1145/2666356.2594299 http://doi.org/10.1145/2666356.2594299 . .

Z Aung , , , W Zaw . . Permission-based Android malware detection . . Int J Sci Technol Res , , 2013 . . 2 ( ( 3 ): ): 228 - - 234 . . . .

A Bartel , , , J Klein , , , Y Le Traon , , , 等 . . Automatically securing permission-based software by reducing the attack surface: an application to Android . . Proc 27 th IEEE/ ACM Int Conf on Automated Software Engineering , , 2012 . . p.274 - - 277 . . DOI: 10.1145/2351676.2351722 http://doi.org/10.1145/2351676.2351722 . .

S Bird , , , E Klein , , , E Loper . . Natural language processing with Python—analyzing text with the natural language toolkit . . O'Reilly Media , , 2009 . . .

I Burguera , , , U Zurutuza , , , S Nadjm-Tehrani . . Crowdroid: behavior-based malware detection system for Android . . Proc 1 st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices , , 2011 . . p.15 - - 26 . . DOI: 10.1145/2046614.2046619 http://doi.org/10.1145/2046614.2046619 . .

R Caruana , , , N Karampatziakis , , , A Yessenalina . . An empirical evaluation of supervised learning in high dimensions . . Proc 25 th Int Conf on Machine Learning , , 2008 . . p.96 - - 103 . . DOI: 10.1145/1390156.1390169 http://doi.org/10.1145/1390156.1390169 . .

PPK Chan , , , WK Song . . Static detection of Android malware by using permissions and API calls . . Proc Int Conf on Machine Learning and Cybernetics , , 2014 . . p.82 - - 87 . . DOI: 10.1109/ICMLC.2014.7009096 http://doi.org/10.1109/ICMLC.2014.7009096 . .

TK Chang , , , GH Hwang . . The design and implementation of an application program interface for securing XML documents . . J Syst Softw , , 2007 . . 80 ( ( 8 ): ): 1362 - - 1374 . . DOI: 10.1016/j.jss.2006.10.051 http://doi.org/10.1016/j.jss.2006.10.051 . .

B Chess , , , G McGraw . . Static analysis for security . . IEEE Secur Priv , , 2004 . . 2 ( ( 6 ): ): 76 - - 79 . . DOI: 10.1109/MSP.2004.111 http://doi.org/10.1109/MSP.2004.111 . .

L Deshotels , , , V Notani , , , A Lakhotia . . Droidlegacy: automated familial classification of Android malware . . Proc ACM SIGPLAN on Program Protection and Reverse Engineering Workshop , , 2014 . . Article 3 DOI: 10.1145/2556464.2556467 http://doi.org/10.1145/2556464.2556467 . .

A Desnos . . Androguard . . https://github.com/androguard/androguard https://github.com/androguard/androguard [Accessed on June 29 , , 2015 . . 2015 . .

R Díaz-Uriarte , , , SA de Andrés . . Gene selection and classification of microarray data using random forest . . BMC Bioinform , , 2006 . . 7 3 DOI: 10.1186/1471-2105-7-3 http://doi.org/10.1186/1471-2105-7-3 . .

eBay . . Online shopping . . www.ebay.com http://www.ebay.com [Accessed on Apr. 4, 2016]. , , 2016 . . 2016 . .

P Faruki , , , V Ganmoor , , , V Laxmi , , , 等 . . AndroSimilar: robust statistical feature signature for Android malware detection . . Proc 6 th Int Conf on Security of Information and Networks , , 2013 . . p.152 - - 159 . . DOI: 10.1145/2523514.2523539 http://doi.org/10.1145/2523514.2523539 . .

A Feizollah , , , NB Anuar , , , R Salleh , , , 等 . . A study of machine learning classifiers for anomaly-based mobile botnet detection . . Malays J Comput Sci , , 2013a . . 26 ( ( 4 ): ): 251 - - 265 . . . .

A Feizollah , , , S Shamshirband , , , NB Anuar , , , 等 . . Anomaly detection using cooperative fuzzy logic controller . . Proc 16 th FIRA RoboWorld Congress , , 2013b . . p.220 - - 231 . . DOI: 10.1007/978-3-642-40409-2_19 http://doi.org/10.1007/978-3-642-40409-2_19 . .

A Feizollah , , , NB Anuar , , , R Salleh , , , 等 . . A review on feature selection in mobile malware detection . . Dig Invest , , 2015 . . 13 22 - - 37 . . DOI: 10.1016/j.diin.2015.02.001 http://doi.org/10.1016/j.diin.2015.02.001 . .

A Feizollah , , , NB Anuar , , , R Salleh , , , 等 . . Androdialysis: analysis of Android intent effectiveness in malware detection . . Comput Secur , , 2017 . . 65 121 - - 134 . . DOI: 10.1016/j.cose.2016.11.007 http://doi.org/10.1016/j.cose.2016.11.007 . .

Y Feng , , , S Anand , , , I Dillig , , , 等 . . Apposcopy: semantics-based detection of Android malware through static analysis . . Proc 22 nd ACM SIGSOFT Int Symp on Foundations of Software Engineering , , 2014 . . p.576 - - 587 . . DOI: 10.1145/2635868.2635869 http://doi.org/10.1145/2635868.2635869 . .

A Firdaus , , , NB Anuar . . Root-exploit malware detection using static analysis and machine learning . . Proc 4 th Int Conf on Computer Science and Computational Mathematics , , 2015 . . p.177 - - 183 . . . .

E Frank , , , MA Hall , , , IH Witten . . The WEKA Workbench (4 th Ed.) . . Morgan Kaufmann. http://www.cs.waikato.ac.nz/ml/WEKA/Witten_et_al_2016_appendix.pdf http://www.cs.waikato.ac.nz/ml/WEKA/Witten_et_al_2016_appendix.pdf , , 2016 . . .

H Fröhlich , , , O Chapelle , , , B Schölkopf . . Feature selection for support vector machines by means of genetic algorithm . . Proc 15 th IEEE Int Conf on Tools with Artificial Intelligence , , 2003 . . p.142 - - 148 . . DOI: 10.1109/TAI.2003.1250182 http://doi.org/10.1109/TAI.2003.1250182 . .

H Gascon , , , F Yamaguchi , , , D Arp , , , 等 . . Structural detection of Android malware using embedded call graphs . . Proc ACM Workshop on Artificial Intelligence and Security , , 2013 . . p.45 - - 54 . . DOI: 10.1145/2517312.2517315 http://doi.org/10.1145/2517312.2517315 . .

DE Goldberg , , , JH Holland . . Genetic algorithms and machine learning . . Mach Learn , , 1988 . . 3 ( ( 2-3 ): ): 95 - - 99 . . DOI: 10.1023/A:1022602019183 http://doi.org/10.1023/A:1022602019183 . .

Google . . Google play store . . https://play.google.com/store?hl=en https://play.google.com/store?hl=en [Accessed on Jan. 1, 2014] , , 2014 . . .

MI Gordon , , , D Kim , , , J Perkins , , , 等 . . Information-flow analysis of Android applications in droidSafe . . Proc Network and Distributed System Security Symp , , 2015 . . p.8 - - 11 . . . .

M Grace , , , YJ Zhou , , , Z Wang , , , 等 . . Systematic detection of capability leaks in stock Android smartphones . . Proc 19 th Network and Distributed System Security Symp , , 2012a . . p.1 - - 15 . . . .

M Grace , , , W Zhou , , , XX Jiang , , , 等 . . Unsafe exposure analysis of mobile in-app advertisements . . Proc 5 th ACM Conf on Security and Privacy in Wireless and Mobile Networks , , 2012b . . p.101 - - 112 . . DOI: 10.1145/2185448.2185464 http://doi.org/10.1145/2185448.2185464 . .

M Grace , , , YJ Zhou , , , Q Zhang , , , 等 . . RiskRanker: scalable and accurate zero-day Android malware detection . . Proc 10 th Int Conf on Mobile Systems, Applications, and Services , , 2012c . . p.281 - - 294 . . DOI: 10.1145/2307636.2307663 http://doi.org/10.1145/2307636.2307663 . .

M Hall , , , E Frank , , , G Holmes , , , 等 . . The WEKA data mining software: an update . . ACM SIGKDD Explor Newsl , , 2009 . . 11 ( ( 1 ): ): 10 - - 18 . . DOI: 10.1145/1656274.1656278 http://doi.org/10.1145/1656274.1656278 . .

CY Huang , , , YT Tsai , , , CH Hsu . . Performance evaluation on permission-based detection for Android malware . . Proc Int Computer Symp , , 2013 . . p.111 - - 120 . . DOI: 10.1007/978-3-642-35473-1_12 http://doi.org/10.1007/978-3-642-35473-1_12 . .

JJ Huang , , , XY Zhang , , , L Tan , , , 等 . . AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction . . Proc 36 th Int Conf on Software Engineering , , 2014 . . p.1036 - - 1046 . . DOI: 10.1145/2568225.2568301 http://doi.org/10.1145/2568225.2568301 . .

A Ikinci , , , T Holz , , , F Freiling . . Monkey-spider: detecting malicious websites with low-interaction honeyclients . . Proc Sicherheit-Schutz und Zuverl?ssigkeit , , 2008 . . p.407 - - 421 . . . .

M Junaid , , , DG Liu , , , D Kung . . Dexteroid: detecting malicious behaviors in Android apps using reverseengineered life cycle models . . Comput Secur , , 2016 . . 59 92 - - 117 . . DOI: 10.1016/j.cose.2016.01.008 http://doi.org/10.1016/j.cose.2016.01.008 . .

H Kang , , , JW Jang , , , A Mohaisen , , , 等 . . Detecting and classifying Android malware using static analysis along with creator information . . Int J Distr Sens Netw , , 2015 . . 11 ( ( 6 ): ): Article 7 DOI: 10.1155/2015/479174 http://doi.org/10.1155/2015/479174 . .

A Karim , , , RB Salleh , , , M Shiraz , , , 等 . . Botnet detection techniques: review, future trends, and issues . . J Zhejiang Univ Sci-C (Comput & Elcetron) , , 2014 . . 15 ( ( 11 ): ): 943 - - 983 . . DOI: 10.1631/jzus.C1300242 http://doi.org/10.1631/jzus.C1300242 . .

A Karim , , , R Salleh , , , MK Khan . . Smartbot: a behavioral analysis framework augmented with machine learning to identify mobile botnet applications . . PLoS ONE , , 2016 . . 11 ( ( 3 ): ): e0150077 DOI: 10.1371/journal.pone.0150077 http://doi.org/10.1371/journal.pone.0150077 . .

AS Khatavakhotan , , , SH Ow . . Development of a software risk management model using unique features of a proposed audit component . . Malays J Comput Sci , , 2015 . . 28 ( ( 2 ): ): 110 - - 131 . . . .

O Komili . . Sophos detects 100% of Android malware in independent test—for the sixth time in a row . . https://blogs.sophos.com/2015/08/14/sophos-detects-100-of-Android-malware-in-independent-test-for-the-sixth-time-in-a-row/ https://blogs.sophos.com/2015/08/14/sophos-detects-100-of-Android-malware-in-independent-test-for-the-sixth-time-in-a-row/ [Accessed on Jan. 1, 2016] , , 2015 . . .

SB Kotsiantis . . Decision trees: a recent overview . . Artif Intell Rev , , 2013 . . 39 ( ( 4 ): ): 261 - - 283 . . DOI: 10.1007/s10462-011-9272-4 http://doi.org/10.1007/s10462-011-9272-4 . .

SB Kotsiantis , , , ID Zaharakis , , , PE Pintelas . . Machine learning: a review of classification and combining techniques . . Artif Intell Rev , , 2006 . . 26 ( ( 3 ): ): 159 - - 190 . . DOI: 10.1007/s10462-007-9052-3 http://doi.org/10.1007/s10462-007-9052-3 . .

GC La Delfa , , , S Monteleone , , , V Catania , , , 等 . . Performance analysis of visualmarkers for indoor navigation systems . . Front Inform Technol Electron Eng , , 2016 . . 17 ( ( 8 ): ): 730 - - 740 . . DOI: 10.1631/FITEE.1500324 http://doi.org/10.1631/FITEE.1500324 . .

HJ Lai , , , Y Tang , , , HX Luo , , , 等 . . Greedy feature selection for ranking . . Proc 15 th Int Conf on Computer Supported Cooperative Work in Design , , 2011 . . p.42 - - 46 . . DOI: 10.1109/CSCWD.2011.5960053 http://doi.org/10.1109/CSCWD.2011.5960053 . .

J Lee , , , S Lee , , , H Lee . . Screening smartphone applications using malware family signatures . . Comput Secur , , 2015 . . 52 234 - - 249 . . DOI: 10.1016/j.cose.2015.02.003 http://doi.org/10.1016/j.cose.2015.02.003 . .

SH Lee , , , SH Jin . . Warning system for detecting malicious applications on Android system . . Int J Comput Commun Eng , , 2013 . . 2 ( ( 3 ): ): 324 - - 327 . . DOI: 10.7763/IJCCE.2013.V2.197 http://doi.org/10.7763/IJCCE.2013.V2.197 . .

SY Liang , , , AW Keep , , , M Might , , , 等 . . Sound and precise malware analysis for Android via pushdown reachability and entry-point saturation . . Proc 3 th ACM Workshop on Security and Privacy in Smartphones & Mobile Devices , , 2013 . . p.21 - - 32 . . DOI: 10.1145/2516760.2516769 http://doi.org/10.1145/2516760.2516769 . .

R Lippmann . . An introduction to computing with neural nets . . IEEE ASSP Mag , , 1987 . . 4 ( ( 2 ): ): 4 - - 22 . . DOI: 10.1109/MASSP.1987.1165576 http://doi.org/10.1109/MASSP.1987.1165576 . .

L Lu , , , ZC Li , , , ZY Wu , , , 等 . . CHEX: statically vetting Android apps for component hijacking vulnerabilities . . Proc ACM Conf on Computer and Communications Security , , 2012 . . p.229 - - 240 . . DOI: 10.1145/2382196.2382223 http://doi.org/10.1145/2382196.2382223 . .

MJ Middlemiss , , , G Dick . . Weighted feature extraction using a genetic algorithm for intrusion detection . . Proc Congress on Evolutionary Computation , , 2003 . . p.1669 - - 1675 . . DOI: 10.1109/CEC.2003.1299873 http://doi.org/10.1109/CEC.2003.1299873 . .

FA Narudin , , , A Feizollah , , , NB Anuar , , , 等 . . Evaluation of machine learning classifiers for mobile malware detection . . Soft Comput , , 2016 . . 20 ( ( 1 ): ): 343 - - 357 . . DOI: 10.1007/s00500-014-1511-6 http://doi.org/10.1007/s00500-014-1511-6 . .

N Peiravian , , , XQ Zhu . . Machine learning for Android malware detection using permission and API calls . . Proc 25 th Int Conf on Tools with Artificial Intelligence , , 2013 . . p.300 - - 305 . . DOI: 10.1109/ICTAI.2013.53 http://doi.org/10.1109/ICTAI.2013.53 . .

H Peng , , , C Gates , , , B Sarma , , , 等 . . Using probabilistic generative models for ranking risks of Android apps . . Proc ACM Conf on Computer and Communications Security , , 2012 . . p.241 - - 252 . . DOI: 10.1145/2382196.2382224 http://doi.org/10.1145/2382196.2382224 . .

WFⅢ Punch , , , ED Goodman , , , M Pei , , , 等 . . Further research on feature selection and classification using genetic algorithms . . Proc 5 th Int Conf on Genetic Algorithms , , 1993 . . p.557 - - 564 . . . .

S Rasthofer , , , S Arzt , , , E Bodden . . A machine-learning approach for classifying and categorizing Android sources and sinks . . Proc Network and Distributed System Security Symp , , 2014 . . p.1 - - 15 . . . .

MFA Razak , , , NB Anuar , , , R Salleh , , , 等 . . The rise of "malware": bibliometric analysis of malware study . . J Netw Comput Appl , , 2016 . . 75 58 - - 76 . . DOI: 10.1016/j.jnca.2016.08.022 http://doi.org/10.1016/j.jnca.2016.08.022 . .

MA Russon . . Android malware discovered on Google Play has infected millions of users with spyware . . http://www.ibtimes.co.uk/Android-malware-discovered-google-play-store-1553341 http://www.ibtimes.co.uk/Android-malware-discovered-google-play-store-1553341 [Accessed on June 13, 2016] , , 2016 . . .

J Sahs , , , L Khan . . A machine learning approach to Android malware detection . . Proc European Intelligence and Security Informatics Conf , , 2012 . . p.141 - - 147 . . DOI: 10.1109/EISIC.2012.34 http://doi.org/10.1109/EISIC.2012.34 . .

AAA Samra , , , K Yim , , , OA Ghanem . . Analysis of clustering technique in Android malware detection . . Proc 7 th Int Conf on Innovative Mobile and Internet Services in Ubiquitous Computing , , 2013 . . p.729 - - 733 . . DOI: 10.1109/IMIS.2013.111 http://doi.org/10.1109/IMIS.2013.111 . .

B Sanz , , , I Santos , , , C Laorden , , , 等 . . PUMA: permission usage to detect malware in Android . . Int Joint Conf CISIS'12-ICEUTE'12-SOCO'12 Special Sessions. Springer Berlin Heidelberg , , 2013a . . p.289 - - 298 . . . .

B Sanz , , , I Santos , , , C Laorden , , , 等 . . Mama: manifest analysis for malware detection in Android . . Cybern Syst , , 2013b . . 44 ( ( 6-7 ): ): 469 - - 488 . . DOI: 10.1080/01969722.2013.803889 http://doi.org/10.1080/01969722.2013.803889 . .

AG Sarip , , , MB Hafez , , , MN Daud . . Application of fuzzy regression model for real estate price prediction . . Malays J Comput Sci , , 2016 . . 29 ( ( 1 ): ): 15 - - 27 . . DOI: 10.22452/mjcs.vol29no1.2 http://doi.org/10.22452/mjcs.vol29no1.2 . .

BP Sarma , , , NH Li , , , C Gates , , , 等 . . Android permissions: a perspective combining risks and benefits . . Proc 17 th ACM Symp on Access Control Models and Technologies , , 2012 . . p.13 - - 22 . . DOI: 10.1145/2295136.2295141 http://doi.org/10.1145/2295136.2295141 . .

AD Schmidt , , , R Bye , , , HG Schmidt , , , 等 . . Static analysis of executables for collaborative malware detection on Android . . Proc IEEE Int Conf on Communications , , 2009a . . p.1 - - 5 . . DOI: 10.1109/ICC.2009.5199486 http://doi.org/10.1109/ICC.2009.5199486 . .

AD Schmidt , , , HG Schmidt , , , L Batyuk , , , 等 . . Smartphone malware evolution revisited: Android next target? . . Proc 4 th Int Conf on Malicious and Unwanted Software , , 2009b . . p.1 - - 7 . . DOI: 10.1109/MALWARE.2009.5403026 http://doi.org/10.1109/MALWARE.2009.5403026 . .

J Schneider . . Cross validation . . http://www.cs.cmu.edu/~schneide/tut5/node42.html http://www.cs.cmu.edu/~schneide/tut5/node42.html [Accessed on Aug. 1, 2016] , , 2016 . . .

SH Seo , , , A Gupta , , , A Mohamed Sallam , , , 等 . . Detecting mobile malware threats to homeland security through static analysis . . J Netw Comput Appl , , 2014 . . 38 43 - - 53 . . DOI: 10.1016/j.jnca.2013.05.008 http://doi.org/10.1016/j.jnca.2013.05.008 . .

A Shabtai , , , Y Fledel , , , Y Elovici . . Automated static code analysis for classifying Android applications using machine learning . . Proc Int Conf on Computational Intelligence and Security , , 2010 . . p.329 - - 333 . . DOI: 10.1109/CIS.2010.77 http://doi.org/10.1109/CIS.2010.77 . .

A Shabtai , , , U Kanonov , , , Y Elovici , , , 等 . . "Andromaly": a behavioral malware detection framework for Android devices . . J Intell Inform Syst , , 2012 . . 38 ( ( 1 ): ): 161 - - 190 . . DOI: 10.1007/s10844-010-0148-x http://doi.org/10.1007/s10844-010-0148-x . .

M Sharif , , , V Yegneswaran , , , H Saidi , , , 等 . . Eureka: a framework for enabling static malware analysis . . Proc 13 th Symp on Research in Computer Security , , 2008 . . p.481 - - 500 . . DOI: 10.1007/978-3-540-88313-5_31 http://doi.org/10.1007/978-3-540-88313-5_31 . .

S Sheen , , , R Anitha , , , V Natarajan . . Android based malware detection using a multifeature collaborative decision fusion approach . . Neurocomputing , , 2015 . . 151 905 - - 912 . . DOI: 10.1016/j.neucom.2014.10.004 http://doi.org/10.1016/j.neucom.2014.10.004 . .

Skylot . . Jadx . . https://github.com/skylot/jadx https://github.com/skylot/jadx , , 2015 . . .

G Stein , , , B Chen , , , AS Wu , , , 等 . . Decision tree classifier for network intrusion detection with GA-based feature selection . . Proc 43 rd Annual Southeast Regional Conf , , 2005 . . p.136 - - 141 . . DOI: 10.1145/1167253.1167288 http://doi.org/10.1145/1167253.1167288 . .

G Suarez-Tangil , , , JE Tapiador , , , P Peris-Lopez , , , 等 . . Dendroid: a text mining approach to analyzing and classifying code structures in Android malware families . . Expert Syst Appl , , 2014 . . 41 ( ( 4 ): ): 1104 - - 1117 . . DOI: 10.1016/j.eswa.2013.07.106 http://doi.org/10.1016/j.eswa.2013.07.106 . .

KA Talha , , , DI Alper , , , C Aydin . . Apk auditor: permission-based Android malware detection system . . Dig Invest , , 2015 . . 13 1 - - 14 . . DOI: 10.1016/j.diin.2015.01.001 http://doi.org/10.1016/j.diin.2015.01.001 . .

P Thomas . . Google's Android operating system dominates the smartphone market . . http://finance.yahoo.com/news/google-Android-operating-system-dominates-170640913.html http://finance.yahoo.com/news/google-Android-operating-system-dominates-170640913.html [Accessed on June 11, 2016] , , 2015 . . .

JA Tropp . . Greed is good: algorithmic results for sparse approximation . . IEEE Trans Inform Theory , , 2004 . . 50 ( ( 10 ): ): 2231 - - 2242 . . DOI: 10.1109/TIT.2004.834793 http://doi.org/10.1109/TIT.2004.834793 . .

A Walenstein , , , L Deshotels , , , A Lakhotia . . Program structure-based feature selection for Android malware analysis . . Proc 4 th Int Conf on Security and Privacy in Mobile Information and Communication Systems , , 2012 . . p.51 - - 52 . . DOI: 10.1007/978-3-642-33392-7_5 http://doi.org/10.1007/978-3-642-33392-7_5 . .

G Williams . . ARFF data . . http://datamining.togaware.com/survivor/ARFF_Data0.html http://datamining.togaware.com/survivor/ARFF_Data0.html [Accessed on Sept. 10, 2015] , , 2010 . . .

DJ Wu , , , CH Mao , , , TE Wei , , , 等 . . Droidmat: Android malware detection through manifest and API calls tracing . . Proc 7 th Asia Joint Conf on Information Security , , 2012 . . p.62 - - 69 . . DOI: 10.1109/AsiaJCIS.2012.18 http://doi.org/10.1109/AsiaJCIS.2012.18 . .

ZM Yang , , , M Yang . . LeakMiner: detect information leakage on Android with static taint analysis . . Proc 3 rd World Congress on Software Engineering , , 2012 . . p.101 - - 104 . . DOI: 10.1109/WCSE.2012.26 http://doi.org/10.1109/WCSE.2012.26 . .

SY Yerima , , , S Sezer , , , G McWilliams , , , 等 . . A new Android malware detection approach using Bayesian classification . . Proc IEEE 27 th Int Conf on Advanced Information Networking and Applications , , 2013 . . p.121 - - 128 . . DOI: 10.1109/AINA.2013.88 http://doi.org/10.1109/AINA.2013.88 . .

SY Yerima , , , S Sezer , , , G McWilliams . . Analysis of Bayesian classification-based approaches for Android malware detection . . IET Inform Secur , , 2014a . . 8 ( ( 1 ): ): 25 - - 36 . . DOI: 10.1049/iet-ifs.2013.0095 http://doi.org/10.1049/iet-ifs.2013.0095 . .

SY Yerima , , , S Sezer , , , I Muttik . . Android malware detection using parallel machine learning classifiers . . Proc 8 th Int Conf on Next Generation Mobile Apps, Services and Technologies , , 2014b . . p.37 - - 42 . . DOI: 10.1109/NGMAST.2014.23 http://doi.org/10.1109/NGMAST.2014.23 . .

SY Yerima , , , S Sezer , , , I Muttik . . High accuracy Android malware detection using ensemble learning . . IET Inform Secur , , 2015 . . 9 ( ( 6 ): ): 313 - - 320 . . DOI: 10.1049/iet-ifs.2014.0099 http://doi.org/10.1049/iet-ifs.2014.0099 . .

L Yu , , , ZL Pan , , , JJ Liu , , , 等 . . Android malware detection technology based on improved Bayesian classification . . Proc 23 rd Int Conf on Instrumentation, Measurement, Computer, Communication and Control , , 2013 . . p.1338 - - 1341 . . DOI: 10.1109/IMCCC.2013.297 http://doi.org/10.1109/IMCCC.2013.297 . .

LS Zhang , , , Y Niu , , , X Wu , , , 等 . . A3: automatic analysis of Android malware . . Proc 1 st Int Workshop on Cloud Computing and Information Security , , 2013 . . p.89 - - 93 . . DOI: 10.2991/ccis-13.2013.22 http://doi.org/10.2991/ccis-13.2013.22 . .

T Zhang . . On the consistency of feature selection using greedy least squares regression . . J Mach Learn Res , , 2009 . . 10 555 - - 568 . . . .

W Zhou , , , YJ Zhou , , , XX Jiang , , , 等 . . Detecting repackaged smartphone applications in third-party Android marketplaces . . Proc 2 nd ACM Conf on Data and Application Security and Privacy , , 2012 . . p.317 - - 326 . . DOI: 10.1145/2133601.2133640 http://doi.org/10.1145/2133601.2133640 . .

W Zhou , , , YJ Zhou , , , M Grace , , , 等 . . Fast, scalable detection of "Piggybacked" mobile applications . . Proc 2 nd ACM Conf on Data and Application Security and Privacy , , 2013 . . p.185 - - 196 . . DOI: 10.1145/2435349.2435377 http://doi.org/10.1145/2435349.2435377 . .

T Zia , , , MP Akhter , , , Q Abbas . . Comparative study of feature selection approaches for Urdu text categorization . . Malays J Comput Sci , , 2015 . . 28 ( ( 2 ): ): 93 - - 109 . . . .

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