HIT4Mal: Hybrid image transformation for malware classification-Reference-Cited by-同舟云学术

HIT4Mal: Hybrid image transformation for malware classification

Published:2019-11-20 Issue:11 Volume:31 Page:
ISSN:2161-3915
Container-title:Transactions on Emerging Telecommunications Technologies
language:en
Short-container-title:Trans Emerging Tel Tech

Author:

Vu Duc‐Ly¹,Nguyen Trong‐Kha²,Nguyen Tam V.³,Nguyen Tu N.⁴,Massacci Fabio¹,Phung Phu H.³^ORCID

Affiliation:

1. Department of Information Engineering and Computer Science University of Trento Trento Italy

2. Department of Computer Science Hongik University Seoul South Korea

3. Department of Computer Science University of Dayton Dayton Ohio

4. Department of Computer Science Purdue University Fort Wayne Fort Wayne Indiana

Funder

NeCS: European Network for Cyber Security

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.3789

Reference66 articles.

1. Kaspersky Lab.Kaspersky Lab number of the year: 360 000 malicious files detected daily in 2017.2019.https://usa.kaspersky.com/about/press-releases/2017_kaspersky-lab-number-of-the-year

2. A Survey on Malware Detection Using Data Mining Techniques

3. AndersonHS KharkarA FilarB RothP.Evading machine learning malware detection. In: Proceedings of the Black Hat USA 2017;2017;Las Vegas NV.

4. WagnerM FischerF LuhR et al.A survey of visualization systems for malware analysis. In: Proceedings of the Eurographics Conference on Visualization (EuroVis) – STARs;2015;Cagliari Italy.

5. NatarajL KarthikeyanS JacobG ManjunathBS.Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security (VizSec '11);2011;Pittsburgh PA.

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