Malware and Average Individual-Reference-Cited by-同舟云学术

Malware and Average Individual

Published:2022-12-09 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)
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Author:

Alrawili Reem¹,Oliva Michael²,Honnef Amber²,Sawall Emily²,AlQahtani Ali Abdullah S.³

Affiliation:

1. North Carolina A&T State University,Applied Science and Technology,Greensboro,North Carolina,USA

2. Computer Science University of Wisconsin-Green Bay,Green Bay,Wisconsin,USA

3. North Carolina A&T State University Greensboro,Computer Systems Technology,North Carolina,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10013949/10013945/10014080.pdf?arnumber=10014080

Reference23 articles.

1. Exploring the use of static and dynamic analysis to improve the performance of the mining sandbox approach for android malware identification

2. MLMD—A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm

3. EPMDroid: Efficient and privacy-preserving malware detection based on SGX through data fusion

4. Advance malware analysis using static and dynamic methodology;2018 International Conference on Advanced Computation and Telecommunication (ICACAT),2018

5. Marvolo: Programmatic data augmentation for practical ml-driven malware detection;wong,2022

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