TY  - INPR
ID  - SisLab3987
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3987/
A1  - Hoang, Dang Kien
A1  - Nguyen, Dai Tho
A1  - Vu, Duy Loi
Y1  - 2020///
N2  - IoT devices play an important role in the industrial revolution 4.0. However, this type of device may exhibit specific security vulnerabilities that can be easily exploited to cause botnet attacks and other malicious activities. In this paper, we introduce a new method for
classification and clustering of IoT malware behaviors
through system call monitoring. Our method is constructed
from multiple one-class SVM classifiers and has the ability to classify known malware with F1-Score over 98% and probability to detect unknown malware up to 97%. Unknown malware instances with similar behaviors can also be grouped together so new classes of malware will be discovered.
TI  - IoT Malware Classification Based on System Calls
M2  - Ho Chi Minh
AV  - none
T2  - The 2020 IEEE-RIVF International Conference on Computing and Communications Technologies
ER  -