Ensemble feature selection with discriminative and representative properties for malware detection

Xiao Yu Zhang; Shupeng Wang; Lei Zhang; Chunjie Zhang; Changsheng Li

doi:10.1109/INFCOMW.2016.7562161

Ensemble feature selection with discriminative and representative properties for malware detection

Xiao Yu Zhang
, Shupeng Wang^*
, Lei Zhang
, Chunjie Zhang
, Changsheng Li

^*此作品的通讯作者

CAS - Institute of Information Engineering
University of Chinese Academy of Sciences
IBM

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

5 引用（Scopus）

摘要

Malware data are typically depicted with extremely high-dimensional features, which lays an excessive computational burden on detection methods. For the sake of effectiveness and efficiency, feature selection is an indispensable part for malware detection. In this paper, we propose an ensemble feature selection method with integration of discriminative and representative properties for malware detection. Based on the labeled and unlabeled data, the most discriminative and representative features are selected, respectively. The former extracts the features that are most distinctive with respect to the classes, and the latter focuses on the features that best represent the data. A comprehensive metric is subsequently obtained, which retains the most informative features.

源语言	英语
主期刊名	2016 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016
出版商	Institute of Electrical and Electronics Engineers Inc.
页	674-675
页数	2
ISBN（电子版）	9781467399555
DOI	https://doi.org/10.1109/INFCOMW.2016.7562161
出版状态	已出版 - 6 9月 2016
已对外发布	是
活动	35th IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016 - San Francisco, 美国期限: 10 4月 2016 → 14 4月 2016

出版系列

姓名	Proceedings - IEEE INFOCOM
卷	2016-September
ISSN（印刷版）	0743-166X

会议

会议	35th IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016
国家/地区	美国
市	San Francisco
时期	10/04/16 → 14/04/16

访问文件

10.1109/INFCOMW.2016.7562161

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, X. Y., Wang, S., Zhang, L., Zhang, C., & Li, C. (2016). Ensemble feature selection with discriminative and representative properties for malware detection. 在 2016 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016 (页码 674-675). 文章 7562161 (Proceedings - IEEE INFOCOM; 卷 2016-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFCOMW.2016.7562161

@inproceedings{3b6e236cc06041b485a17c7752e6c41f,

title = "Ensemble feature selection with discriminative and representative properties for malware detection",

abstract = "Malware data are typically depicted with extremely high-dimensional features, which lays an excessive computational burden on detection methods. For the sake of effectiveness and efficiency, feature selection is an indispensable part for malware detection. In this paper, we propose an ensemble feature selection method with integration of discriminative and representative properties for malware detection. Based on the labeled and unlabeled data, the most discriminative and representative features are selected, respectively. The former extracts the features that are most distinctive with respect to the classes, and the latter focuses on the features that best represent the data. A comprehensive metric is subsequently obtained, which retains the most informative features.",

keywords = "discriminative feature, feature selection, malware detection, matrix optimization, representative feature",

author = "Zhang, \{Xiao Yu\} and Shupeng Wang and Lei Zhang and Chunjie Zhang and Changsheng Li",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 35th IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016 ; Conference date: 10-04-2016 Through 14-04-2016",

year = "2016",

month = sep,

day = "6",

doi = "10.1109/INFCOMW.2016.7562161",

language = "English",

series = "Proceedings - IEEE INFOCOM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "674--675",

booktitle = "2016 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016",

address = "United States",

}

Zhang, XY, Wang, S, Zhang, L, Zhang, C & Li, C 2016, Ensemble feature selection with discriminative and representative properties for malware detection. 在 2016 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016., 7562161, Proceedings - IEEE INFOCOM, 卷 2016-September, Institute of Electrical and Electronics Engineers Inc., 页码 674-675, 35th IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016, San Francisco, 美国, 10/04/16. https://doi.org/10.1109/INFCOMW.2016.7562161

Ensemble feature selection with discriminative and representative properties for malware detection. / Zhang, Xiao Yu; Wang, Shupeng; Zhang, Lei 等.
2016 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 页码 674-675 7562161 (Proceedings - IEEE INFOCOM; 卷 2016-September).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Ensemble feature selection with discriminative and representative properties for malware detection

AU - Zhang, Xiao Yu

AU - Wang, Shupeng

AU - Zhang, Lei

AU - Zhang, Chunjie

AU - Li, Changsheng

PY - 2016/9/6

Y1 - 2016/9/6

N2 - Malware data are typically depicted with extremely high-dimensional features, which lays an excessive computational burden on detection methods. For the sake of effectiveness and efficiency, feature selection is an indispensable part for malware detection. In this paper, we propose an ensemble feature selection method with integration of discriminative and representative properties for malware detection. Based on the labeled and unlabeled data, the most discriminative and representative features are selected, respectively. The former extracts the features that are most distinctive with respect to the classes, and the latter focuses on the features that best represent the data. A comprehensive metric is subsequently obtained, which retains the most informative features.

AB - Malware data are typically depicted with extremely high-dimensional features, which lays an excessive computational burden on detection methods. For the sake of effectiveness and efficiency, feature selection is an indispensable part for malware detection. In this paper, we propose an ensemble feature selection method with integration of discriminative and representative properties for malware detection. Based on the labeled and unlabeled data, the most discriminative and representative features are selected, respectively. The former extracts the features that are most distinctive with respect to the classes, and the latter focuses on the features that best represent the data. A comprehensive metric is subsequently obtained, which retains the most informative features.

KW - discriminative feature

KW - feature selection

KW - malware detection

KW - matrix optimization

KW - representative feature

UR - https://www.scopus.com/pages/publications/84988908550

U2 - 10.1109/INFCOMW.2016.7562161

DO - 10.1109/INFCOMW.2016.7562161

M3 - Conference contribution

AN - SCOPUS:84988908550

T3 - Proceedings - IEEE INFOCOM

SP - 674

EP - 675

BT - 2016 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 35th IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016

Y2 - 10 April 2016 through 14 April 2016

ER -

Zhang XY, Wang S, Zhang L, Zhang C, Li C. Ensemble feature selection with discriminative and representative properties for malware detection. 在 2016 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 页码 674-675. 7562161. (Proceedings - IEEE INFOCOM). doi: 10.1109/INFCOMW.2016.7562161

Ensemble feature selection with discriminative and representative properties for malware detection

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