An approach to fast malware classification based on malware's meta-data using machine learning technique

情報処理学会

書誌事項

タイトル別名
  • 機械学習の手法を用いたメタデータによるマルウェアの高速な分類方法

抄録

With the rapid increase in malware, it is important for malware analysis that classifying unknown malware files into malware families to characterize the type of behavior and static malware characteristic accuracy. In this paper we introduce an approach to fast malware classification based on malware's file meta-data. We used a machine learning technique called decision tree algorithm to classify malware rapidly and correctly. Experimental results with the malware samples show that our system successfully determined some semantic similarity between malware and showed their inner similarity in behavior and static malware characteristic.

With the rapid increase in malware, it is important for malware analysis that classifying unknown malware files into malware families to characterize the type of behavior and static malware characteristic accuracy. In this paper we introduce an approach to fast malware classification based on malware's file meta-data. We used a machine learning technique called decision tree algorithm to classify malware rapidly and correctly. Experimental results with the malware samples show that our system successfully determined some semantic similarity between malware and showed their inner similarity in behavior and static malware characteristic.

収録刊行物

キーワード

詳細情報 詳細情報について

  • CRID
    1050855522091238016
  • NII論文ID
    170000067574
  • Web Site
    http://id.nii.ac.jp/1001/00078029/
  • 本文言語コード
    en
  • 資料種別
    conference paper
  • データソース種別
    • IRDB
    • CiNii Articles

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