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Author:

Lai, Yingxu (Lai, Yingxu.) (Scholars:赖英旭) | Liu, Hongnan (Liu, Hongnan.) | Yang, Zhen (Yang, Zhen.) (Scholars:杨震)

Indexed by:

EI Scopus

Abstract:

Differing in traditional methods which extracted too much features or filtered valuable items, we proposed a feature representation and extraction method based on LZW compression algorithm to detect malicious codes. The compression algorithm not only reduces the number of features, but also is enough to cover malicious codes. In this paper, we described the process of our feature extraction in detail, including 0-data processing, fix-length coding and threshold setting. The experimental results show that our method outperforms other methods based on Bayes and SVM in DR and AR. © 2013 Springer-Verlag GmbH.

Keyword:

Feature extraction Codes (symbols) Malware Extraction

Author Community:

  • [ 1 ] [Lai, Yingxu]College of Computer Science, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Liu, Hongnan]College of Computer Science, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Yang, Zhen]College of Computer Science, Beijing University of Technology, Beijing 100124, China

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Source :

ISSN: 1876-1100

Year: 2013

Issue: VOL. 1

Volume: 156 LNEE

Page: 211-218

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 9

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