Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics

• URL: http://arxiv.org/abs/2509.03290v1
• Date: Wed, 03 Sep 2025 13:18:41 GMT
• Title: Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics
• Authors: Babak Azkaei, Kishor Chandra Joshi, George Exarchakos,
• Abstract summary: We propose two actionable anomaly detection algorithms tailored for real-world deployment.<n>The first algorithm identifies user equipment (UE) at risk of severe throughput degradation by analyzing key performance indicators.<n>The second algorithm evaluates neighbor cell radio coverage quality, filtering out cells with anomalous signal strength or interference levels.
• Score: 0.8119699312788383
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ever-increasing reliance of critical services on network infrastructure coupled with the increased operational complexity of beyond-5G/6G networks necessitate the need for proactive and automated network fault management. The provision for open interfaces among different radio access network\,(RAN) elements and the integration of AI/ML into network architecture enabled by the Open RAN\,(O-RAN) specifications bring new possibilities for active network health monitoring and anomaly detection. In this paper we leverage these advantages and develop an anomaly detection framework that proactively detect the possible throughput drops for a UE and minimize the post-handover failures. We propose two actionable anomaly detection algorithms tailored for real-world deployment. The first algorithm identifies user equipment (UE) at risk of severe throughput degradation by analyzing key performance indicators (KPIs) such as resource block utilization and signal quality metrics, enabling proactive handover initiation. The second algorithm evaluates neighbor cell radio coverage quality, filtering out cells with anomalous signal strength or interference levels. This reduces candidate targets for handover by 41.27\% on average. Together, these methods mitigate post-handover failures and throughput drops while operating much faster than the near-real-time latency constraints. This paves the way for self-healing 6G networks.

Related papers

• Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks [77.17664010626726]
  We focus on the routing with multiple UAV clusters in low-altitude intelligent networks (LAINs)<n>To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques.<n>We show that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks.
  arXiv  Detail & Related papers  (2026-02-27T04:30:35Z)
• Self-Supervised Learning of Graph Representations for Network Intrusion Detection [6.453778601809096]
  GraphIDS is a self-supervised intrusion detection model that unifies representation learning and anomaly detection.<n>An inductive graph neural network embeds each flow with its local topological context to capture typical network behavior.<n>A Transformer-based encoder-decoder reconstructs these embeddings, implicitly learning global co-occurrence patterns via self-attention.<n>During inference, flows with unusually high reconstruction errors are flagged as potential intrusions.
  arXiv  Detail & Related papers  (2025-09-20T11:02:50Z)
• AI/ML Life Cycle Management for Interoperable AI Native RAN [50.61227317567369]
  Artificial intelligence (AI) and machine learning (ML) models are rapidly permeating the 5G Radio Access Network (RAN)<n>These developments lay the foundation for AI-native transceivers as a key enabler for 6G.
  arXiv  Detail & Related papers  (2025-07-24T16:04:59Z)
• Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis [8.805162150763847]
  This paper presents an enhanced experimental model that integrates a self-attention mechanism with a Recurrent Neural Network (RNN)-based autoencoder. Our approach is grounded in time-series analysis, processes in-phase and quadrature (I/Q) samples to identify irregularities that could indicate potential jamming attacks. The model's architecture, augmented with a self-attention layer, extends the capabilities of RNN autoencoders.
  arXiv  Detail & Related papers  (2024-11-05T07:01:15Z)
• DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs [10.215216950059874]
  jamming attacks pose substantial risks to the 5G network.<n>This work presents a novel deep learning-based technique for detecting jammers in CAV networks.<n>Results show that the proposed method achieves 96.4% detection rate in extra low jamming power.
  arXiv  Detail & Related papers  (2024-03-05T04:29:31Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection [0.0]
  unsupervised anomaly-based deep learning detection system called ARCADE. A convolutional Autoencoder (AE) is proposed that suits online detection in resource-constrained environments.
  arXiv  Detail & Related papers  (2022-05-03T11:47:36Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming [97.40955121478716]
  We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations. We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively. We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
  arXiv  Detail & Related papers  (2020-10-22T12:32:29Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks [17.2528983535773]
  This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN. It can detect and classify four types of jamming attacks: constant jamming, random jamming, jamming, and reactive jamming. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.
  arXiv  Detail & Related papers  (2020-04-13T17:21:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.