• DocumentCode
    1790413
  • Title

    Improved malicious code classification considering sequence by machine learning

  • Author

    Paik, Incheon

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Univ. of Aizu, Aizu-Wakamatsu, Japan
  • fYear
    2014
  • fDate
    22-25 June 2014
  • Firstpage
    1
  • Lastpage
    2
  • Abstract
    Classification of malicious code by machine learning gives more flexible and adaptable prediction result than by existing approaches [1]. But the approach just can identify looks-like malicious code instead of real malicious one. In this research, a novel method to reduce the vagueness in the classification by machine learning to consider code sequence.
  • Keywords
    learning (artificial intelligence); pattern classification; support vector machines; SVM approach; code sequence; improved malicious code classification; machine learning; support vector machine; vagueness reduction; Accuracy; Educational institutions; Support vector machine classification; Syntactics; Training; Vectors; classification; machine learning; malicious Web code;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Consumer Electronics (ISCE 2014), The 18th IEEE International Symposium on
  • Conference_Location
    JeJu Island
  • Type

    conf

  • DOI
    10.1109/ISCE.2014.6884429
  • Filename
    6884429