Breakthrough to Adaptive and Cost-Aware Hardware-Assisted Zero-Day Malware Detection: A Reinforcement Learning-Based Approach

Author:

He Zhangying1,Makrani Hosein Mohammadi2,Rafatirad Setareh3,Homayoun Houman2,Sayadi Hossein1

Affiliation:

1. California State University,Department of Computer Engineering and Computer Science,Long Beach,CA,USA

2. University of California,Department of Electrical and Computer Engineering,Davis,CA,USA

3. University of California,Department of Computer Science,Davis,CA,USA

Publisher

IEEE

Cited by 5 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. Redefining Trust: Assessing Reliability of Machine Learning Algorithms in Intrusion Detection Systems;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

2. Intelligent Malware Detection based on Hardware Performance Counters: A Comprehensive Survey;2024 25th International Symposium on Quality Electronic Design (ISQED);2024-04-03

3. Cyber-Immunity at the Core: Securing Biomedical Devices through Hardware-Level Machine Learning Defense;2023 IEEE Biomedical Circuits and Systems Conference (BioCAS);2023-10-19

4. Image-Based Zero-Day Malware Detection in IoMT Devices: A Hybrid AI-Enabled Method;2023 24th International Symposium on Quality Electronic Design (ISQED);2023-04-05

5. Malware Speaks! Deep Learning Based Assembly Code Processing for Detecting Evasive Cryptojacking;IEEE Transactions on Dependable and Secure Computing;2023

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