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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.09706 |
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| _version_ | 1866911203624747008 |
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| author | Rahman, Md Habibur Hossen, Md Sharif Stephenson, Nathan H. Shah, Vijay K. Da Silva, Aloizio |
| author_facet | Rahman, Md Habibur Hossen, Md Sharif Stephenson, Nathan H. Shah, Vijay K. Da Silva, Aloizio |
| contents | The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN, which can severely undermine network performance, is jamming attacks. This paper presents SAJD- a self-adaptive jammer detection framework that autonomously detects jamming attacks in AI/ML framework-integrated ORAN environments without human intervention. The SAJD framework forms a closed-loop system that includes near-realtime inference of radio signal jamming via our developed ML-based xApp, as well as continuous monitoring and retraining pipelines through rApps. In this demonstration, we will show how SAJD outperforms state-of-the-art jamming detection xApp (offline trained with manual labels) in terms of accuracy and adaptability under various dynamic and previously unseen interference scenarios in the O-RAN-compliant testbed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09706 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN Rahman, Md Habibur Hossen, Md Sharif Stephenson, Nathan H. Shah, Vijay K. Da Silva, Aloizio Cryptography and Security Artificial Intelligence The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN, which can severely undermine network performance, is jamming attacks. This paper presents SAJD- a self-adaptive jammer detection framework that autonomously detects jamming attacks in AI/ML framework-integrated ORAN environments without human intervention. The SAJD framework forms a closed-loop system that includes near-realtime inference of radio signal jamming via our developed ML-based xApp, as well as continuous monitoring and retraining pipelines through rApps. In this demonstration, we will show how SAJD outperforms state-of-the-art jamming detection xApp (offline trained with manual labels) in terms of accuracy and adaptability under various dynamic and previously unseen interference scenarios in the O-RAN-compliant testbed. |
| title | A Demonstration of Self-Adaptive Jamming Attack Detection in AI/ML Integrated O-RAN |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2510.09706 |