• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
搜索

Author:

Lai, Ying-Xu (Lai, Ying-Xu.) (Scholars:赖英旭)

Indexed by:

EI Scopus PKU CSCD

Abstract:

Machine learning or data mining method can identify new or unknown malicious executables with some degree of success. Feature selection is a key to applying data mining or machine learning to detect malicious executables. In order to improve detecting accuracy, a new method of extracting most representative features is purposed. The new classifier based on strings achieves has high detection rates and can be expected to perform well in real-world conditions.

Keyword:

Data mining Feature extraction Machine learning

Author Community:

  • [ 1 ] [Lai, Ying-Xu]College of Computer Science, Beijing University of Technology, Beijing 100124, China

Reprint Author's Address:

  • 赖英旭

Email:

Show more details

Related Keywords:

Related Article:

Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2009

Issue: 12

Volume: 35

Page: 1703-1709

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: 3

Online/Total:362/10625803
Address:BJUT Library(100 Pingleyuan,Chaoyang District,Beijing 100124, China Post Code:100124) Contact Us:010-67392185
Copyright:BJUT Library Technical Support:Beijing Aegean Software Co., Ltd.