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Journal Central

@article{ART002536972},
author={Ye-Seul Lee and Hyunl-Jae Choi and Dong-Myung Shin and Lee Jung Jae},
title={Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware},
journal={Journal of Software Assessment and Valuation},
issn={2092-8114},
year={2019},
volume={15},
number={2},
pages={43-50},
doi={10.29056/jsav.2019.12.06}

TY - JOUR
AU - Ye-Seul Lee
AU - Hyunl-Jae Choi
AU - Dong-Myung Shin
AU - Lee Jung Jae
TI - Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware
JO - Journal of Software Assessment and Valuation
PY - 2019
VL - 15
IS - 2
PB - Korea Software Assessment and Valuation Society
SP - 43
EP - 50
SN - 2092-8114
AB - 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.
KW - Abnomal behavior detection;Anomaly Detection;Ransomware;Deep Learning;Performance Comparison;CNN-LSTM
DO - 10.29056/jsav.2019.12.06
ER -

Ye-Seul Lee, Hyunl-Jae Choi, Dong-Myung Shin and Lee Jung Jae. (2019). Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware. Journal of Software Assessment and Valuation, 15(2), 43-50.

Ye-Seul Lee, Hyunl-Jae Choi, Dong-Myung Shin and Lee Jung Jae. 2019, "Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware", Journal of Software Assessment and Valuation, vol.15, no.2 pp.43-50. Available from: doi:10.29056/jsav.2019.12.06

Ye-Seul Lee, Hyunl-Jae Choi, Dong-Myung Shin, Lee Jung Jae "Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware" Journal of Software Assessment and Valuation 15.2 pp.43-50 (2019) : 43.

Ye-Seul Lee, Hyunl-Jae Choi, Dong-Myung Shin, Lee Jung Jae. Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware. 2019; 15(2), 43-50. Available from: doi:10.29056/jsav.2019.12.06

Ye-Seul Lee, Hyunl-Jae Choi, Dong-Myung Shin and Lee Jung Jae. "Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware" Journal of Software Assessment and Valuation 15, no.2 (2019) : 43-50.doi: 10.29056/jsav.2019.12.06

Ye-Seul Lee; Hyunl-Jae Choi; Dong-Myung Shin; Lee Jung Jae. Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware. Journal of Software Assessment and Valuation, 15(2), 43-50. doi: 10.29056/jsav.2019.12.06

Ye-Seul Lee; Hyunl-Jae Choi; Dong-Myung Shin; Lee Jung Jae. Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware. Journal of Software Assessment and Valuation. 2019; 15(2) 43-50. doi: 10.29056/jsav.2019.12.06

Ye-Seul Lee, Hyunl-Jae Choi, Dong-Myung Shin, Lee Jung Jae. Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware. 2019; 15(2), 43-50. Available from: doi:10.29056/jsav.2019.12.06

Ye-Seul Lee, Hyunl-Jae Choi, Dong-Myung Shin and Lee Jung Jae. "Deep Learning based User Anomaly Detection Performance Evaluation to prevent Ransomware" Journal of Software Assessment and Valuation 15, no.2 (2019) : 43-50.doi: 10.29056/jsav.2019.12.06