第2期 张玉玲，等：依特征频率的安卓恶意软件异常检测的研究 ·173· BISYS).Lake District,UK,2012:281-294. [18]HASTIE T,TIBSHIRANI R,FRIEDMAN J.Unsuper- [10]WU Songyang,WANG Pan,LI Xun,et al.Effective detec- vised learning[M]//HASTIE T,TIBSHIRANI R.FRIED- tion of android malware based on the usage of data flow MAN J.The Elements of Statistical Learning.New York, APIs and machine learning[J].Information and software USA:Springer,2009:485-585. technology,.2016,75:17-25. [I9]CRISTIANINI N,SHAWE-TAYLOR J.支持向量机导论 [11]YUAN Zhenlong,LU Yongqiang,WANG Zhaoguo,et al. [M.李国正，译.北京：电子工业出版社，2004：57-61. Droid-Sec:deep learning in android malware detection[C/ CRISTIANINI N,SHAWE-TAYLOR J.An introduction Proceedings of the 2014 ACM Conference on SIGCOMM. to support vector machines and other kernel-based learn- Chicago,Illinois,USA.2014:371-372. ing methods[M].LI Guozheng,Trans.Beijing:Publishing [12]SHEEN S,ANITHA R,NATARAJAN V.Android based House of Electronics Industry,2004:57-61. malware detection using a multifeature collaborative de- [20罗隽，丁力，潘志松，等.异常检测中频率敏感的单分类 cision fusion approach[J].Neurocomputing,2015,151: 算法研究[).计算机研究与发展，2007,44(Z2):235-239. 905-912. LUO Jun,DING Li,PAN Zhisong,et al.Research on se- [13]TAM K,KHAN S J,FATTORI A,et al.CopperDroid: quence-call-frequency-based one-class algorithm in abnor- automatic reconstruction of android malware behaviors mal detection[J].Journal of computer research and devel- [OL/EB]/.[2016-03-24].https://www.researchgate.net/ opment,.2007,44(Z2:235-239. publication/300925104. [21]张玉玲，尹传环.基于SVM的安卓恶意软件检测).山 [14]BURGUERA L,ZURUTUZA U,NADJM-TEHRANI S. 东大学学报：工学版，2017,471)42-47. Crowdroid:behavior-based malware detection system for ZHANG Yuling,YIN Chuanhuan.Android malware detec- android[C]//Proceedings of the Ist ACM Workshop on Se- tion based on SVM[J].Journal of Shandong university:en- gineering science,2017,47(1):42-47. curity and Privacy in Smartphones and Mobile Devices. Chicago,Illinois,USA,2011:15-26. 作者简介： [15]TAM K,KHAN S J,FATTORI A,et al.CopperDroid: 张玉玲，女，1990年生，硕士研究 Automatic Reconstruction of Android Malware Behaviors[Cl/ 生，主要研究方向为机器学习。 Proceedings of Annual Network and Distributed System Security (NDSS).San Diego,United States,2015. [16]FARUKI P,BHANDARI S,LAXMI V,et al.DroidAna- lyst:synergic app framework for static and dynamic app analysis[M]//ABIELMONA R,FALCON R,ZINCIR- HEYWOOD N,et al.Recent Advances in Computational 尹传环，男，1976年生，副教授， 主要研究方向为网络安全（入侵检 Intelligence in Defense and Security.Cham:Springer, 测)、数据挖掘、机器学习（支持向量 2016:519-552 机)。 [17]TAX M J D,DUIN ROBERT P W.Support vector do- main description[J].Pattern recognition letters,1999, 20(11/12/13):1191-1199BISYS). Lake District, UK, 2012: 281–294. WU Songyang, WANG Pan, LI Xun, et al. Effective detec￾tion of android malware based on the usage of data flow APIs and machine learning[J]. Information and software technology, 2016, 75: 17–25. [10] YUAN Zhenlong, LU Yongqiang, WANG Zhaoguo, et al. Droid-Sec: deep learning in android malware detection[C]// Proceedings of the 2014 ACM Conference on SIGCOMM. Chicago, Illinois, USA, 2014: 371–372. [11] SHEEN S, ANITHA R, NATARAJAN V. Android based malware detection using a multifeature collaborative de￾cision fusion approach[J]. Neurocomputing, 2015, 151: 905–912. [12] TAM K, KHAN S J, FATTORI A, et al. CopperDroid: automatic reconstruction of android malware behaviors [OL/EB]/. [2016-03-24]. https://www.researchgate.net/ publication/300925104. [13] BURGUERA L, ZURUTUZA U, NADJM-TEHRANI S. Crowdroid: behavior-based malware detection system for android[C]//Proceedings of the 1st ACM Workshop on Se￾curity and Privacy in Smartphones and Mobile Devices. Chicago, Illinois, USA, 2011: 15–26. [14] TAM K, KHAN S J, FATTORI A, et al. CopperDroid: Automatic Reconstruction of Android Malware Behaviors[C]// Proceedings of Annual Network and Distributed System Security (NDSS). San Diego, United States, 2015. [15] FARUKI P, BHANDARI S, LAXMI V, et al. DroidAna￾lyst: synergic app framework for static and dynamic app analysis[M]//ABIELMONA R, FALCON R, ZINCIR￾HEYWOOD N, et al. Recent Advances in Computational Intelligence in Defense and Security. Cham: Springer, 2016: 519–552. [16] TAX M J D, DUIN ROBERT P W. Support vector do￾main description[J]. Pattern recognition letters, 1999, 20(11/12/13): 1191–1199. [17] HASTIE T, TIBSHIRANI R, FRIEDMAN J. Unsuper￾vised learning[M]//HASTIE T, TIBSHIRANI R, FRIED￾MAN J. The Elements of Statistical Learning. New York, USA: Springer, 2009: 485–585. [18] CRISTIANINI N, SHAWE-TAYLOR J. 支持向量机导论 [M]. 李国正,译. 北京: 电子工业出版社, 2004: 57–61. CRISTIANINI N, SHAWE-TAYLOR J. An introduction to support vector machines and other kernel-based learn￾ing methods[M]. LI Guozheng, Trans. Beijing: Publishing House of Electronics Industry, 2004: 57–61. [19] 罗隽, 丁力, 潘志松, 等. 异常检测中频率敏感的单分类 算法研究[J]. 计算机研究与发展, 2007, 44(Z2): 235–239. LUO Jun, DING Li, PAN Zhisong, et al. Research on se￾quence-call-frequency-based one-class algorithm in abnor￾mal detection[J]. Journal of computer research and devel￾opment, 2007, 44(Z2): 235–239. [20] 张玉玲, 尹传环. 基于 SVM 的安卓恶意软件检测[J]. 山 东大学学报: 工学版, 2017, 47(1): 42–47. ZHANG Yuling, YIN Chuanhuan. Android malware detec￾tion based on SVM[J]. Journal of Shandong university: en￾gineering science, 2017, 47(1): 42–47. [21] 作者简介： 张玉玲，女，1990 年生，硕士研究 生，主要研究方向为机器学习。 尹传环，男，1976 年生，副教授， 主要研究方向为网络安全 (入侵检 测)、数据挖掘、机器学习 (支持向量 机)。 第 2 期 张玉玲，等：依特征频率的安卓恶意软件异常检测的研究 ·173·