Multi-view Representation Learning from Malware to Defend Against Adversarial Variants-Reference-Cited by-同舟云学术

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Multi-view Representation Learning from Malware to Defend Against Adversarial Variants

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Data Mining Workshops (ICDMW)
language:
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Author:

Hu James Lee¹,Ebrahimi Mohammadreza²,Li Weifeng³,Li Xin⁴,Chen Hsinchun¹

Affiliation:

1. University of Arizona,Department of Management Information Systems,Tucson,USA

2. University of South Florida,School of Information Systems and Management,Tampa,USA

3. University of Georgia,Department of Management Information Systems,Athens,USA

4. University of Arizona,Department of Computer Science,Tucson,USA

Funder

NSF

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10029378/10029381/10031219.pdf?arnumber=10031219

Reference30 articles.

1. A survey of multi-view machine learning

2. Learning to evade static pe machine learning malware models via reinforcement learning;anderson;ArXiv Preprint,2018

3. A multi-view context-aware approach to Android malware detection and malicious code localization

4. Clustering-Aided Multi-View Classification: A Case Study on Android Malware Detection

5. Security Evaluation of Pattern Classifiers under Attack

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hash-Based Function Call Graph Fusion Method for IoT Malware Detection;2023 26th International Symposium on Wireless Personal Multimedia Communications (WPMC);2023-11-19

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