Privacy-Preserving and Syscall-Based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks-Reference-Cited by-同舟云学术

Privacy-Preserving and Syscall-Based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks

Published:2023-05-15 Issue:10 Volume:10 Page:8408-8415
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Huertas Celdrán Alberto¹^ORCID,Sánchez Sánchez Pedro Miguel²^ORCID,Feng Chao¹^ORCID,Bovet Gérôme³^ORCID,Pérez Gregorio Martínez¹^ORCID,Stiller Burkhard¹^ORCID

Affiliation:

1. Department of Informatics, Communication Systems Group, University of Zurich, Zürich, Switzerland

2. Department of Information and Communications Engineering, University of Murcia, Murcia, Spain

3. Cyber-Defence Campus, Armasuisse Science and Technology, Thun, Switzerland

Funder

Swiss Federal Office for Defense Procurement (armasuisse) with the CyberTracer and RESERVE Projects

University of Zurich

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/6488907/10119650/09916265.pdf?arnumber=9916265

Reference25 articles.

1. Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors

2. Detection Strategy Against Restricted SSDF Attack With Potential Interaction Assistance

3. Federated Learning: Challenges, Methods, and Future Directions

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