Identifying stealth malware using CPU power consumption and learning algorithms-Reference-Cited by-同舟云学术

Identifying stealth malware using CPU power consumption and learning algorithms

Published:2018-08-09 Issue:5 Volume:26 Page:589-613
ISSN:1875-8924
Container-title:Journal of Computer Security
language:
Short-container-title:JCS

Author:

Luckett Patrick¹,McDonald J. Todd¹,Glisson William B.²,Benton Ryan¹,Dawson Joel¹,Doyle Blair A.¹

Affiliation:

1. School of Computing, University of South Alabama, Mobile, Al, United States. E-mails: phl801@jagmail.southalabama.edu, jtmcdonald@southalabama.edu, rbenton@southalabama.edu, jad1324@jagmail.southalabama.edu, bad1524@jagmail.southalabama.edu

2. Department of Computer Science, Sam Houston State University, Huntsville, TX, United States. E-mail: glisson@shsu.edu

Publisher

IOS Press

Subject

Computer Networks and Communications,Hardware and Architecture,Safety, Risk, Reliability and Quality,Software

Reference48 articles.

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3. B. Arslan, S. Gunduz and S. Sagiroglu, A review on mobile threats and machine learning based detection approaches, in: Digital Forensic and Security (ISDFS), 2016 4th International Symposium on, IEEE, 2016, pp. 7–13.

4. Investigating the Impact of Global Positioning System Evidence

5. Why deep learning works: A manifold disentanglement perspective;Brahma;IEEE transactions on neural networks and learning systems,2016

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