Learning from big malwares

Linhai Song; Heqing Huang; Wu Zhou; Wenfei Wu; Yiying Zhang

doi:10.1145/2967360.2967367

Learning from big malwares

Linhai Song, Heqing Huang, Wu Zhou, Wenfei Wu, Yiying Zhang

College of Information Sciences and Technology

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

10 Scopus citations

Abstract

This paper calls for the attention to investigate real-world malwares in large scales by examining the largest real malware repository, VirusTotal. As a first step, we analyzed two fundamental characteristics of Windows executable malwares from VirusTotal. We designed offline and online tools for this analysis. Our results show that malwares appear in bursts and that distributions of malwares are highly skewed.

Original language	English (US)
Title of host publication	Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016
Publisher	Association for Computing Machinery, Inc
ISBN (Electronic)	9781450342650
DOIs	https://doi.org/10.1145/2967360.2967367
State	Published - Aug 4 2016
Event	7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016 - Hong Kong, China Duration: Aug 4 2016 → Aug 5 2016

Publication series

Name	Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016

Other

Other	7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016
Country/Territory	China
City	Hong Kong
Period	8/4/16 → 8/5/16

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture

Access to Document

10.1145/2967360.2967367

Cite this

@inproceedings{8199c3f51e664d0f96df5eda797969ab,

title = "Learning from big malwares",

abstract = "This paper calls for the attention to investigate real-world malwares in large scales by examining the largest real malware repository, VirusTotal. As a first step, we analyzed two fundamental characteristics of Windows executable malwares from VirusTotal. We designed offline and online tools for this analysis. Our results show that malwares appear in bursts and that distributions of malwares are highly skewed.",

author = "Linhai Song and Heqing Huang and Wu Zhou and Wenfei Wu and Yiying Zhang",

note = "Publisher Copyright: Copyright {\textcopyright} 2016 ACM.; 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016 ; Conference date: 04-08-2016 Through 05-08-2016",

year = "2016",

month = aug,

day = "4",

doi = "10.1145/2967360.2967367",

language = "English (US)",

series = "Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016",

publisher = "Association for Computing Machinery, Inc",

booktitle = "Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016",

}

Song, L, Huang, H, Zhou, W, Wu, W & Zhang, Y 2016, Learning from big malwares. in Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016., 2967367, Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016, Association for Computing Machinery, Inc, 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016, Hong Kong, China, 8/4/16. https://doi.org/10.1145/2967360.2967367

Learning from big malwares. / Song, Linhai; Huang, Heqing; Zhou, Wu et al.
Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016. Association for Computing Machinery, Inc, 2016. 2967367 (Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016).

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

TY - GEN

T1 - Learning from big malwares

AU - Song, Linhai

AU - Huang, Heqing

AU - Zhou, Wu

AU - Wu, Wenfei

AU - Zhang, Yiying

PY - 2016/8/4

Y1 - 2016/8/4

N2 - This paper calls for the attention to investigate real-world malwares in large scales by examining the largest real malware repository, VirusTotal. As a first step, we analyzed two fundamental characteristics of Windows executable malwares from VirusTotal. We designed offline and online tools for this analysis. Our results show that malwares appear in bursts and that distributions of malwares are highly skewed.

AB - This paper calls for the attention to investigate real-world malwares in large scales by examining the largest real malware repository, VirusTotal. As a first step, we analyzed two fundamental characteristics of Windows executable malwares from VirusTotal. We designed offline and online tools for this analysis. Our results show that malwares appear in bursts and that distributions of malwares are highly skewed.

UR - http://www.scopus.com/inward/record.url?scp=84986593933&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84986593933&partnerID=8YFLogxK

U2 - 10.1145/2967360.2967367

DO - 10.1145/2967360.2967367

M3 - Conference contribution

AN - SCOPUS:84986593933

T3 - Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016

BT - Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016

PB - Association for Computing Machinery, Inc

T2 - 7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016

Y2 - 4 August 2016 through 5 August 2016

ER -

Learning from big malwares

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this