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Author:

Lai, Yingxu (Lai, Yingxu.) (Scholars:赖英旭) | Liu, Zhenghui (Liu, Zhenghui.)

Indexed by:

CPCI-S EI Scopus

Abstract:

Analyzed Bayesian classifier with string, n-gram and API as features, we found that it is very difficult to improve Bayesian classifier detection accuracy because selected features are not completely independent. In order to solve this problem, we propose a new improved choose features method which are most representative properties, and show that our method achieve high detection rates, even on completely new, previously unseen malicious executables. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

Keyword:

detection malicious codes Bayesian algorithm

Author Community:

  • [ 1 ] [Lai, Yingxu]Beijing Univ Technol, Coll Comp Sci, Beijing 100124, Peoples R China
  • [ 2 ] [Liu, Zhenghui]Beijing Univ Technol, Coll Comp Sci, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Liu, Zhenghui]Beijing Vocat Coll Elect Sci, Sci & Technol Engn Fac, Beijing 100029, Peoples R China

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Source :

CEIS 2011

ISSN: 1877-7058

Year: 2011

Volume: 15

Language: English

Cited Count:

WoS CC Cited Count: 2

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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