A deep learning approach for detecting covert timing channel attacks using sequential data

Authors

Shorouq Al-Eidi, Tafila Technical University, Jordan
Omar Darwish, Eastern Michigan University
Yuanzhu Chen, Queen’s University, Canada
Majdi Maabreh, The Hashemite University, Jordan
Yahya Tashtoush, Jordan University of Science and Technology

Document Type

Article

Publication Date

2024

Department/School

Information Security and Applied Computing

Publication Title

Cluster Computing

Comments

O. Darwish is a faculty member in EMU's School of Information Security and Applied Computing.

Link to Published Version

DOI: 10.1007/s10586-023-04035-5

Recommended Citation

Al-Eidi, S., Darwish, O., Chen, Y., Maabreh, M., & Tashtoush, Y. (2024). A deep learning approach for detecting covert timing channel attacks using sequential data. Cluster Computing, 27, 1655–1665. https://doi.org/10.1007/s10586-023-04035-5