The algorithm of malicious code detection based on data mining

Yubo Yang; Yang Zhao; Xiabi Liu

doi:10.1063/1.4992960

The algorithm of malicious code detection based on data mining

Yubo Yang^*, Yang Zhao, Xiabi Liu

^*Corresponding author for this work

School of Computer Science and Technology

LTD

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

1 Citation (Scopus)

Abstract

Traditional technology of malicious code detection has low accuracy and it has insufficient detection capability for new variants. In terms of malicious code detection technology which is based on the data mining, its indicators are not accurate enough, and its classification detection efficiency is relatively low. This paper proposed the information gain ratio indicator based on the N-gram to choose signature, this indicator can accurately reflect the detection weight of the signature, and helped by C4.5 decision tree to elevate the algorithm of classification detection.

Original language	English
Title of host publication	Green Energy and Sustainable Development I
Subtitle of host publication	Proceedings of the International Conference on Green Energy and Sustainable Development, GESD 2017
Editors	Jun Xiao, Lin Liu, Jianfeng Ke
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735415423
DOIs	https://doi.org/10.1063/1.4992960
Publication status	Published - 31 Jul 2017
Event	2017 International Conference on Green Energy and Sustainable Development, GESD 2017 - Chongqing City, China Duration: 27 May 2017 → 28 May 2017

Publication series

Name	AIP Conference Proceedings
Volume	1864
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	2017 International Conference on Green Energy and Sustainable Development, GESD 2017
Country/Territory	China
City	Chongqing City
Period	27/05/17 → 28/05/17

Keywords

Data Mining
Decision Tree
Information Gain
Malicious Code

Access to Document

10.1063/1.4992960

Cite this

Yang, Y., Zhao, Y., & Liu, X. (2017). The algorithm of malicious code detection based on data mining. In J. Xiao, L. Liu, & J. Ke (Eds.), Green Energy and Sustainable Development I: Proceedings of the International Conference on Green Energy and Sustainable Development, GESD 2017 Article 020143 (AIP Conference Proceedings; Vol. 1864). American Institute of Physics Inc.. https://doi.org/10.1063/1.4992960

@inproceedings{4b4cb07b7f1f4fbe8867f2abc9188902,

title = "The algorithm of malicious code detection based on data mining",

abstract = "Traditional technology of malicious code detection has low accuracy and it has insufficient detection capability for new variants. In terms of malicious code detection technology which is based on the data mining, its indicators are not accurate enough, and its classification detection efficiency is relatively low. This paper proposed the information gain ratio indicator based on the N-gram to choose signature, this indicator can accurately reflect the detection weight of the signature, and helped by C4.5 decision tree to elevate the algorithm of classification detection.",

keywords = "Data Mining, Decision Tree, Information Gain, Malicious Code",

author = "Yubo Yang and Yang Zhao and Xiabi Liu",

note = "Publisher Copyright: {\textcopyright} 2017 Author(s).; 2017 International Conference on Green Energy and Sustainable Development, GESD 2017 ; Conference date: 27-05-2017 Through 28-05-2017",

year = "2017",

month = jul,

day = "31",

doi = "10.1063/1.4992960",

language = "English",

series = "AIP Conference Proceedings",

publisher = "American Institute of Physics Inc.",

editor = "Jun Xiao and Lin Liu and Jianfeng Ke",

booktitle = "Green Energy and Sustainable Development I",

address = "United States",

}

Yang, Y, Zhao, Y & Liu, X 2017, The algorithm of malicious code detection based on data mining. in J Xiao, L Liu & J Ke (eds), Green Energy and Sustainable Development I: Proceedings of the International Conference on Green Energy and Sustainable Development, GESD 2017., 020143, AIP Conference Proceedings, vol. 1864, American Institute of Physics Inc., 2017 International Conference on Green Energy and Sustainable Development, GESD 2017, Chongqing City, China, 27/05/17. https://doi.org/10.1063/1.4992960

The algorithm of malicious code detection based on data mining. / Yang, Yubo; Zhao, Yang; Liu, Xiabi.
Green Energy and Sustainable Development I: Proceedings of the International Conference on Green Energy and Sustainable Development, GESD 2017. ed. / Jun Xiao; Lin Liu; Jianfeng Ke. American Institute of Physics Inc., 2017. 020143 (AIP Conference Proceedings; Vol. 1864).

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

TY - GEN

T1 - The algorithm of malicious code detection based on data mining

AU - Yang, Yubo

AU - Zhao, Yang

AU - Liu, Xiabi

PY - 2017/7/31

Y1 - 2017/7/31

N2 - Traditional technology of malicious code detection has low accuracy and it has insufficient detection capability for new variants. In terms of malicious code detection technology which is based on the data mining, its indicators are not accurate enough, and its classification detection efficiency is relatively low. This paper proposed the information gain ratio indicator based on the N-gram to choose signature, this indicator can accurately reflect the detection weight of the signature, and helped by C4.5 decision tree to elevate the algorithm of classification detection.

AB - Traditional technology of malicious code detection has low accuracy and it has insufficient detection capability for new variants. In terms of malicious code detection technology which is based on the data mining, its indicators are not accurate enough, and its classification detection efficiency is relatively low. This paper proposed the information gain ratio indicator based on the N-gram to choose signature, this indicator can accurately reflect the detection weight of the signature, and helped by C4.5 decision tree to elevate the algorithm of classification detection.

KW - Data Mining

KW - Decision Tree

KW - Information Gain

KW - Malicious Code

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U2 - 10.1063/1.4992960

DO - 10.1063/1.4992960

M3 - Conference contribution

AN - SCOPUS:85029325952

T3 - AIP Conference Proceedings

BT - Green Energy and Sustainable Development I

A2 - Xiao, Jun

A2 - Liu, Lin

A2 - Ke, Jianfeng

PB - American Institute of Physics Inc.

T2 - 2017 International Conference on Green Energy and Sustainable Development, GESD 2017

Y2 - 27 May 2017 through 28 May 2017

ER -

The algorithm of malicious code detection based on data mining

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Keywords

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