Deep Learning-Based Detection for Multiple Cache Side-Channel Attacks-Reference-Cited by-同舟云学术

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Deep Learning-Based Detection for Multiple Cache Side-Channel Attacks

Published:2024 Issue: Volume:19 Page:1672-1686
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Kim Hodong¹^ORCID,Hahn Changhee²^ORCID,Kim Hyunwoo J.¹^ORCID,Shin Youngjoo³^ORCID,Hur Junbeom¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Korea University, Seoul, South Korea

2. Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, South Korea

3. School of Cybersecurity, Korea University, Seoul, South Korea

Funder

Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government through Ministry of Science and ICT

National Research Foundation of Korea (NRF) funded by the Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10345632.pdf?arnumber=10345632

Reference64 articles.

1. Last-Level Cache Side-Channel Attacks are Practical

2. FLUSH+RELOAD: A high resolution, low noise, L3 cache side-channel attack;Yarom

3. Flush+Flush: A Fast and Stealthy Cache Attack

4. Prime+Abort: A timer-free high-precision L3 cache attack using Intel TSX;Disselkoen

5. Analyzing Cache Side Channels Using Deep Neural Networks

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