%0 Conference Paper
%A Tran, Tuan
%A Khanh, Nguyen
%A Le, Son
%A Phan, Anh
%A Mizuhito, Ogawa
%A Nguyen, Minh
%B 2018 10th International Conference on Knowledge and Systems Engineering (KSE) (KSE'18)
%D 2018
%F SisLab:3205
%K malware detection; malware analysis; deep learning; convolutional neural network
%T Comparison of Three Deep Learning-based Approaches for IoT Malware Detection
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3205/
%X The development of IoT brings many opportunities but also many challenges. Recently, increasing more malware has been appeared to target IoT devices. Machine learning is one of the typical techniques used in the detection of malware. In this paper, we survey three approaches for IoT malware detection based on the application of convolutional neural networks on different data representations including sequences, images, and assembly code. The comparison was conducted on the task of distinguishing malware from nonmalware. We also analyze the results to assess the pros/cons of each method.