CANShield: Deep-Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal Level-Reference-Cited by-同舟云学术

CANShield: Deep-Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal Level

Author:

Shahriar Md Hasan¹^ORCID,Xiao Yang²^ORCID,Moriano Pablo³^ORCID,Lou Wenjing¹^ORCID,Hou Y. Thomas⁴^ORCID

Affiliation:

1. Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

2. Department of Computer Science, University of Kentucky, Lexington, KY, USA

3. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

4. Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

Funder

U.S. National Science Foundation

Virginia Commonwealth Cyber Initiative

Laboratory Directed Research and Development Program of Oak Ridge National Laboratory

UT-Battelle, LLC, for the U.S. Department of Energy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

Reference78 articles.

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