From Grim Reality to Practical Solution: Malware Classification in Real-World Noise-Reference-Cited by-同舟云学术

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From Grim Reality to Practical Solution: Malware Classification in Real-World Noise

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Symposium on Security and Privacy (SP)
language:
Short-container-title:

Author:

Wu Xian¹,Guo Wenbo²,Yan Jia³,Coskun Baris⁴,Xing Xinyu¹

Affiliation:

1. Northwestern University

2. UC Berkeley

3. Penn State

4. AWS

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10179215/10179280/10179453.pdf?arnumber=10179453

Reference94 articles.

1. Learning noise transition matrix from only noisy labels via total variation regularization;zhang;ICML,2021

2. What is the effect of importance weighting in deep learning?;byrd;ICML,2019

3. How does disagreement help generalization against label corruption?;yu;ICML,2019

4. Learning from imbalanced data;he;IEEE Transactions on Knowledge and Data Engineering,2009

5. Part-dependent label noise: Towards instance-dependent label noise;xia;NeurIPS,2020

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1. Robust Blind Equalization for NB-IoT Driven by QAM Signals;IEEE Internet of Things Journal;2024-06-15

2. Intrusion Detection for Unmanned Aerial Vehicles Security: A Tiny Machine Learning Model;IEEE Internet of Things Journal;2024-06-15

3. MalOSDF: An Opcode Slice-Based Malware Detection Framework Using Active and Ensemble Learning;Electronics;2024-01-15

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