AI-HydRa: Advanced hybrid approach using random forest and deep learning for malware classification-Reference-Cited by-同舟云学术

AI-HydRa: Advanced hybrid approach using random forest and deep learning for malware classification

Published:2021-02 Issue: Volume:546 Page:420-435
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Yoo Suyeon,Kim Sungjin,Kim Seungjae,Kang Brent Byunghoon

Funder

Office of Naval Research

Institute for Information and Communications Technology Promotion

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference45 articles.

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2. A survey of network anomaly detection techniques;Ahmed;Journal of Network and Computer Applications,2016

3. Anubis, URL:http://anubis.iseclab.org/.

4. G.N. Barbosa, R.R. Branco, Prevalent characteristics in modern malware, black hat USA 2014 (2014).

5. Dynamic analysis of malicious code;Bayer;Journal in Computer Virology,2006

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