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Leveraging UE-Level Collaborative Intelligence for Scalable Jamming Detection in 5G Networks

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Bibliographic Details
Title: Leveraging UE-Level Collaborative Intelligence for Scalable Jamming Detection in 5G Networks
Authors: Xu, Jiali, Loscrì, Valeria
Contributors: Xu, Jiali
Source: 2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). :793-797
Publisher Information: IEEE, 2025.
Publication Year: 2025
Subject Terms: Collaborative Intelligence, [INFO.INFO-MC] Computer Science [cs]/Mobile Computing, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], Distributed Systems, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Anomaly Detection, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [INFO] Computer Science [cs], 5G Security, Edge Intelligence
Description: The proliferation of 5G networks enhances connectivity but also increases vulnerability to threats like jamming. Traditional detection strategies often rely on external hardware capturing RF fingerprints, a method becoming increasingly costly and impractical with expanding 5G coverage and heterogeneous IoT device growth. Our prior research demonstrated the feasibility of on-device jamming detection using native User Equipment (UE) log messages, which provide valuable insights but suffer from limited spatial awareness when used locally. This Work-In-Progress (WIP) paper proposes a novel collaborative framework to overcome these limitations. We outline a system where distributed UE report binary jamming indicators derived from standard signal quality measurements available in device logs. A central or distributed fusion mechanism aggregates these simple inputs to achieve network-wide jamming detection, map the affected area, and estimate the jammer's location. We argue that this approach offers scalability and improved situational awareness compared to local methods, while minimizing UE complexity and communication overhead per device. This paper details the proposed architecture, discusses suitable fusion and localization algorithms adapted for binary data, outlines key challenges, and presents our plan for evaluation.
Document Type: Article
Conference object
File Description: application/pdf
DOI: 10.1109/dcoss-iot65416.2025.00120
Access URL: https://hal.science/hal-05055815v2
Rights: STM Policy #29
Accession Number: edsair.doi.dedup.....0e94d0e95761c9e5e38db63e0e848f96
Database: OpenAIRE
Description
Abstract:The proliferation of 5G networks enhances connectivity but also increases vulnerability to threats like jamming. Traditional detection strategies often rely on external hardware capturing RF fingerprints, a method becoming increasingly costly and impractical with expanding 5G coverage and heterogeneous IoT device growth. Our prior research demonstrated the feasibility of on-device jamming detection using native User Equipment (UE) log messages, which provide valuable insights but suffer from limited spatial awareness when used locally. This Work-In-Progress (WIP) paper proposes a novel collaborative framework to overcome these limitations. We outline a system where distributed UE report binary jamming indicators derived from standard signal quality measurements available in device logs. A central or distributed fusion mechanism aggregates these simple inputs to achieve network-wide jamming detection, map the affected area, and estimate the jammer's location. We argue that this approach offers scalability and improved situational awareness compared to local methods, while minimizing UE complexity and communication overhead per device. This paper details the proposed architecture, discusses suitable fusion and localization algorithms adapted for binary data, outlines key challenges, and presents our plan for evaluation.
DOI:10.1109/dcoss-iot65416.2025.00120