• Title/Summary/Keyword: Abnomal behavior detection

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Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware (랜섬웨어 방지를 위한 딥러닝 기반의 사용자 비정상 행위 탐지 성능 평가)

  • Lee, Ye-Seul;Choi, Hyun-Jae;Shin, Dong-Myung;Lee, Jung-Jae
    • Journal of Software Assessment and Valuation
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    • v.15 no.2
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    • pp.43-50
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    • 2019
  • With the development of IT technology, computer-related crimes are rapidly increasing, and in recent years, the damage to ransomware infections is increasing rapidly at home and abroad. Conventional security solutions are not sufficient to prevent ransomware infections, and to prevent threats such as malware and ransomware that are evolving, a combination of deep learning technologies is needed to detect abnormal behavior and abnormal symptoms. In this paper, a method is proposed to detect user abnormal behavior using CNN-LSTM model and various deep learning models. Among the proposed models, CNN-LSTM model detects user abnormal behavior with 99% accuracy.