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文章基本信息

  • 标题:A Machine Learning based Ransomware Detection Model using a Hybrid Analysis
  • 本地全文:下载
  • 作者:Ji-Won Kim ; Seon-Hak Ji ; Sung-Ryul Kim
  • 期刊名称:Journal of Security Engineering
  • 印刷版ISSN:1738-7531
  • 出版年度:2017
  • 卷号:14
  • 期号:4
  • 页码:263-280
  • 出版社:SERSC
  • 摘要:Ransomware, which recently being highlighted is evolving in some diverse aspects from random attackstargeting civilians continue to specific target (target attack) attack. Ransomware has developed into abusiness-based service RaaS (Ransomware as a Service) model and a new ransomware variant is alsoincreasing.In this paper, we propose a detective model for ransomware through Logistic Regression Analysistechnique using Opcode information, the API information as an independent variable, determine theransomware as a dependent variable. These results show that the hybrid detection rate outperformed thestatic and dynamic detection rate, and the logistic regression technique outperformed the SVM, na飗e Bayestechnique.
  • 关键词:Ransomware; Malware; Machine Learning; Logistic Regression; K-means Clustering; Hybrid Analysis
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