IEICE TRANSACTIONS on Information
Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image
Summary :
We propose an effective 2d image based end-to-end deep learning model for malware detection by introducing a black & white embedding to reserve bit information and adapting the convolution architecture. Experimental results show that our proposed scheme can achieve superior performance in both of training and testing data sets compared to well-known image recognition deep learning models (VGG and ResNet).
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.4 pp.896-900
- Publication Date
- 2020/04/01
- Publicized
- 2019/12/25
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDL8146
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Minkyoung CHO
Myongji Univ.
Jik-Soo KIM
Myongji Univ.
Jongho SHIN
Myongji Univ.
Incheol SHIN
Mokpo Nat'l Univ.
Keyword
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Minkyoung CHO, Jik-Soo KIM, Jongho SHIN, Incheol SHIN, "Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 4, pp. 896-900, April 2020, doi: 10.1587/transinf.2019EDL8146.
Abstract: We propose an effective 2d image based end-to-end deep learning model for malware detection by introducing a black & white embedding to reserve bit information and adapting the convolution architecture. Experimental results show that our proposed scheme can achieve superior performance in both of training and testing data sets compared to well-known image recognition deep learning models (VGG and ResNet).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8146/_p
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@ARTICLE{e103-d_4_896,
author={Minkyoung CHO, Jik-Soo KIM, Jongho SHIN, Incheol SHIN, },
journal={IEICE TRANSACTIONS on Information},
title={Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image},
year={2020},
volume={E103-D},
number={4},
pages={896-900},
abstract={We propose an effective 2d image based end-to-end deep learning model for malware detection by introducing a black & white embedding to reserve bit information and adapting the convolution architecture. Experimental results show that our proposed scheme can achieve superior performance in both of training and testing data sets compared to well-known image recognition deep learning models (VGG and ResNet).},
keywords={},
doi={10.1587/transinf.2019EDL8146},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Mal2d: 2d Based Deep Learning Model for Malware Detection Using Black and White Binary Image
T2 - IEICE TRANSACTIONS on Information
SP - 896
EP - 900
AU - Minkyoung CHO
AU - Jik-Soo KIM
AU - Jongho SHIN
AU - Incheol SHIN
PY - 2020
DO - 10.1587/transinf.2019EDL8146
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2020
AB - We propose an effective 2d image based end-to-end deep learning model for malware detection by introducing a black & white embedding to reserve bit information and adapting the convolution architecture. Experimental results show that our proposed scheme can achieve superior performance in both of training and testing data sets compared to well-known image recognition deep learning models (VGG and ResNet).
ER -
English
