Proceedings of the International Conference on Promotion of Information Technology (ICPIT 2016)

Multiple Classifier Systems for More Accurate JavaScript Malware Detection

Authors
Zibo Yi, Jun Ma, Lei Luo, Jie Yu, Qingbo Wu
Corresponding Author
Zibo Yi
Available Online August 2016.
DOI
https://doi.org/10.2991/icpit-16.2016.22How to use a DOI?
Keywords
machine learning, JavaScript malware detection, multiple classifier system
Abstract
The researches of JavaScript malware detection focus on machine learning techniques in recent years. These works extract features from JavaScript's abstract syntax tree for the training of classifiers and achieve satisfactory detection results. However, in the training set there exist some scripts that are not so representative and may cause occasional incorrect classification. We propose multiple classifier system (MCS) to reduce this kind of misclassification. As shown in the experiments, the accuracy increases because of the MCS while training time is slightly greater than the original classifier.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
International Conference on Promotion of Information Technology (ICPIT 2016)
Part of series
Advances in Computer Science Research
Publication Date
August 2016
ISBN
978-94-6252-219-0
ISSN
2352-538X
DOI
https://doi.org/10.2991/icpit-16.2016.22How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zibo Yi
AU  - Jun Ma
AU  - Lei Luo
AU  - Jie Yu
AU  - Qingbo Wu
PY  - 2016/08
DA  - 2016/08
TI  - Multiple Classifier Systems for More Accurate JavaScript Malware Detection
BT  - International Conference on Promotion of Information Technology (ICPIT 2016)
PB  - Atlantis Press
SP  - 139
EP  - 143
SN  - 2352-538X
UR  - https://doi.org/10.2991/icpit-16.2016.22
DO  - https://doi.org/10.2991/icpit-16.2016.22
ID  - Yi2016/08
ER  -