# Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks

arXiv: https://arxiv.org/abs/2004.06077 · HTML: https://mlsanity.com/paper/2004.06077/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation](https://mlsanity.com/paper/2303.12947/) (2023)
- [DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs](https://mlsanity.com/paper/2403.02645/) (2024)
- [Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines](https://mlsanity.com/paper/2405.09564/) (2024)
- [Anti-Jamming Games in Multi-Band Wireless Ad Hoc Networks](https://mlsanity.com/paper/2111.11178/) (2021)
- [CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets](https://mlsanity.com/paper/2506.15075/) (2025)
- [Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detection](https://mlsanity.com/paper/2605.01705/) (2026)
- [CITADEL: CSI-Based Jamming Detection and Open-Set Classification for IIoT Networks](https://mlsanity.com/paper/2606.22939/) (2026)

## Abstract (arXiv)

In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently by providing real-time cloud infrastructures, cooperative radio, and centralized data processing. Recently, given their vulnerability to malicious attacks, the security of C-RAN networks has attracted significant attention. Among various anomaly-based intrusion detection techniques, the most promising one is the machine learning-based intrusion detection as it learns without human assistance and adjusts actions accordingly. In this direction, many solutions have been proposed, but they show either low accuracy in terms of attack classification or they offer just a single layer of attack detection. This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN that can detect and classify four types of jamming attacks: constant jamming, random jamming, deceptive jamming, and reactive jamming. This deployment enhances security by minimizing the false negatives in C-RAN architectures. The experimental evaluation of the proposed solution is carried out using WSN-DS (Wireless Sensor Networks DataSet), which is a dedicated wireless dataset for intrusion detection. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.