TY - GEN
T1 - Malware detection using malware image and deep learning
AU - Choi, Sunoh
AU - Jang, Sungwook
AU - Kim, Youngsoo
AU - Kim, Jonghyun
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/12
Y1 - 2017/12/12
N2 - These days a lot of malware are generated. In order to deal with the new malware, we need new ways to detect malware. In this paper, we introduce a method to detect malware using deep learning. First, we generate images from benign files and malware. Second, by using deep learning, we train a model to detect malware. Then, by the trained model, we detect malware. By using malware images and deep learning, we can detect malware fast since we do not need any static analysis or dynamic analysis.
AB - These days a lot of malware are generated. In order to deal with the new malware, we need new ways to detect malware. In this paper, we introduce a method to detect malware using deep learning. First, we generate images from benign files and malware. Second, by using deep learning, we train a model to detect malware. Then, by the trained model, we detect malware. By using malware images and deep learning, we can detect malware fast since we do not need any static analysis or dynamic analysis.
KW - Deep Learning
KW - Malware Detection
UR - https://www.scopus.com/pages/publications/85046895149
U2 - 10.1109/ICTC.2017.8190895
DO - 10.1109/ICTC.2017.8190895
M3 - Conference paper
AN - SCOPUS:85046895149
T3 - International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
SP - 1193
EP - 1195
BT - International Conference on Information and Communication Technology Convergence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Information and Communication Technology Convergence, ICTC 2017
Y2 - 18 October 2017 through 20 October 2017
ER -