Support Vector Machine Based on Incremental Learning for Malware Detection

DOI

10.2991/csic-15.2015.49How to use a DOI?

Keywords

Support vector machine, Incremental learning, Malware detection

Abstract

The training of traditional SVM method requires the solution of quadratic programming, and consumes high memory and has low speed for large data training. Incremental learning is one of the meaningful methods to continuously update the data for learning, which keeps the previous learning results, re learning only for the additional data, so as to form a continuous learning process. This paper will study the support vector machine based on incremental learning method and its application in the malware detection. The experiments carried out in the Internet Security Laboratory at Kingsoft Corporation suggested that, for large number of virus samples, our method can rapidly and effectively update the sample features, which avoids duplication of learning history samples and ensures the malware prediction ability for the detection model.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Weiwei Zhuang
AU  - Lei Xiao
AU  - Jianfeng Cui
AU  - WeiChuan Zhuang
PY  - 2015/07
DA  - 2015/07
TI  - Support Vector Machine Based on Incremental Learning for Malware Detection
BT  - Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication
PB  - Atlantis Press
SP  - 204
EP  - 207
SN  - 2352-538X
UR  - https://doi.org/10.2991/csic-15.2015.49
DO  - 10.2991/csic-15.2015.49
ID  - Zhuang2015/07
ER  -