Machine learning for analyzing malware

Yajie Dong; Zhenyan Liu; Yida Yan; Yong Wang; Tu Peng; Ji Zhang

doi:10.1007/978-3-319-64701-2_28

Machine learning for analyzing malware

Yajie Dong^*, Zhenyan Liu, Yida Yan, Yong Wang, Tu Peng, Ji Zhang

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

The Internet has become an indispensable part of people’s work and life. It provides favorable communication conditions for malwares. Therefore, malwares are endless and spread faster and become one of the main threats of current network security. Based on the malware analysis process, from the original feature extraction and feature selection to malware detection, this paper introduces the machine learning algorithm such as clustering, classification and association analysis, and how to use the machine learning algorithm to malware and its variants for effective analysis.

Original language	English
Title of host publication	Network and System Security - 11th International Conference, NSS 2017, Proceedings
Editors	Zheng Yan, Refik Molva, Wojciech Mazurczyk, Raimo Kantola
Publisher	Springer Verlag
Pages	386-398
Number of pages	13
ISBN (Print)	9783319647005
DOIs	https://doi.org/10.1007/978-3-319-64701-2_28
Publication status	Published - 2017
Externally published	Yes
Event	11th International Conference on Network and System Security, NSS 2017 - Helsinki, Finland Duration: 21 Aug 2017 → 23 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10394 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Network and System Security, NSS 2017
Country/Territory	Finland
City	Helsinki
Period	21/08/17 → 23/08/17

Keywords

Analyzing malware
Association analysis
Classification
Clustering
Machine learning

Access to Document

10.1007/978-3-319-64701-2_28

Cite this

Dong, Y., Liu, Z., Yan, Y., Wang, Y., Peng, T., & Zhang, J. (2017). Machine learning for analyzing malware. In Z. Yan, R. Molva, W. Mazurczyk, & R. Kantola (Eds.), Network and System Security - 11th International Conference, NSS 2017, Proceedings (pp. 386-398). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10394 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-64701-2_28

Dong, Yajie ; Liu, Zhenyan ; Yan, Yida et al. / Machine learning for analyzing malware. Network and System Security - 11th International Conference, NSS 2017, Proceedings. editor / Zheng Yan ; Refik Molva ; Wojciech Mazurczyk ; Raimo Kantola. Springer Verlag, 2017. pp. 386-398 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{20cb8c01fb0f456998e7697fd974d7ea,

title = "Machine learning for analyzing malware",

abstract = "The Internet has become an indispensable part of people{\textquoteright}s work and life. It provides favorable communication conditions for malwares. Therefore, malwares are endless and spread faster and become one of the main threats of current network security. Based on the malware analysis process, from the original feature extraction and feature selection to malware detection, this paper introduces the machine learning algorithm such as clustering, classification and association analysis, and how to use the machine learning algorithm to malware and its variants for effective analysis.",

keywords = "Analyzing malware, Association analysis, Classification, Clustering, Machine learning",

author = "Yajie Dong and Zhenyan Liu and Yida Yan and Yong Wang and Tu Peng and Ji Zhang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 11th International Conference on Network and System Security, NSS 2017 ; Conference date: 21-08-2017 Through 23-08-2017",

year = "2017",

doi = "10.1007/978-3-319-64701-2\_28",

language = "English",

isbn = "9783319647005",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "386--398",

editor = "Zheng Yan and Refik Molva and Wojciech Mazurczyk and Raimo Kantola",

booktitle = "Network and System Security - 11th International Conference, NSS 2017, Proceedings",

address = "Germany",

}

Dong, Y, Liu, Z, Yan, Y, Wang, Y , Peng, T & Zhang, J 2017, Machine learning for analyzing malware. in Z Yan, R Molva, W Mazurczyk & R Kantola (eds), Network and System Security - 11th International Conference, NSS 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10394 LNCS, Springer Verlag, pp. 386-398, 11th International Conference on Network and System Security, NSS 2017, Helsinki, Finland, 21/08/17. https://doi.org/10.1007/978-3-319-64701-2_28

Machine learning for analyzing malware. / Dong, Yajie; Liu, Zhenyan; Yan, Yida et al.
Network and System Security - 11th International Conference, NSS 2017, Proceedings. ed. / Zheng Yan; Refik Molva; Wojciech Mazurczyk; Raimo Kantola. Springer Verlag, 2017. p. 386-398 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10394 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine learning for analyzing malware

AU - Dong, Yajie

AU - Liu, Zhenyan

AU - Yan, Yida

AU - Wang, Yong

AU - Peng, Tu

AU - Zhang, Ji

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - The Internet has become an indispensable part of people’s work and life. It provides favorable communication conditions for malwares. Therefore, malwares are endless and spread faster and become one of the main threats of current network security. Based on the malware analysis process, from the original feature extraction and feature selection to malware detection, this paper introduces the machine learning algorithm such as clustering, classification and association analysis, and how to use the machine learning algorithm to malware and its variants for effective analysis.

AB - The Internet has become an indispensable part of people’s work and life. It provides favorable communication conditions for malwares. Therefore, malwares are endless and spread faster and become one of the main threats of current network security. Based on the malware analysis process, from the original feature extraction and feature selection to malware detection, this paper introduces the machine learning algorithm such as clustering, classification and association analysis, and how to use the machine learning algorithm to malware and its variants for effective analysis.

KW - Analyzing malware

KW - Association analysis

KW - Classification

KW - Clustering

KW - Machine learning

UR - https://www.scopus.com/pages/publications/85028466409

U2 - 10.1007/978-3-319-64701-2_28

DO - 10.1007/978-3-319-64701-2_28

M3 - Conference contribution

AN - SCOPUS:85028466409

SN - 9783319647005

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 386

EP - 398

BT - Network and System Security - 11th International Conference, NSS 2017, Proceedings

A2 - Yan, Zheng

A2 - Molva, Refik

A2 - Mazurczyk, Wojciech

A2 - Kantola, Raimo

PB - Springer Verlag

T2 - 11th International Conference on Network and System Security, NSS 2017

Y2 - 21 August 2017 through 23 August 2017

ER -

Dong Y, Liu Z, Yan Y, Wang Y , Peng T , Zhang J. Machine learning for analyzing malware. In Yan Z, Molva R, Mazurczyk W, Kantola R, editors, Network and System Security - 11th International Conference, NSS 2017, Proceedings. Springer Verlag. 2017. p. 386-398. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-64701-2_28

Machine learning for analyzing malware

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Keywords

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