arXiv subjects

Samara Mayhoub

Publications and source records attributed to Samara Mayhoub.

3 recordsLinked to original sources

arXiv2026

Talk Like a Packet: Rethinking Network Traffic Analysis with Transformer Foundation Models

Inspired by the success of Transformer-based models in natural language processing, this paper investigates their potential as foundation models for network traffic analysis. We propose a unified pre-training and fine-tuning pipeline for traffic foundation models. Through fine-tuning, we demonstrate the generalizability of the traffic foundation models in various downstream tasks, including traffic classification, traffic characteristic prediction, and traffic generation. We also compare against non-foundation baselines, demonstrating that the foundation-model backbones achieve improved performance. Moreover, we categorize existing models based on their architecture, input modality, and pre-training strategy. Our findings show that these models can effectively learn traffic representations and perform well with limited labeled datasets, highlighting their potential in future intelligent network analysis systems.

cs.NI↗

arXiv2025

Towards a Multi-Layer Defence Framework for Securing Near-Real-Time Operations in Open RAN

Securing the near-real-time (near-RT) control operations in Open Radio Access Networks (Open RAN) is increasingly critical, yet remains insufficiently addressed, as new runtime threats target the control loop while the system is operational. In this paper, we propose a multi-layer defence framework designed to enhance the security of near-RT RAN Intelligent Controller (RIC) operations. We classify operational-time threats into three categories, message-level, data-level, and control logic-level, and design and implement a dedicated detection and mitigation component for each: a signature-based E2 message inspection module performing structural and semantic validation of signalling exchanges, a telemetry poisoning detector based on temporal anomaly scoring using an LSTM network, and a runtime xApp attestation mechanism based on execution-time hash challenge-response. The framework is evaluated on an O-RAN testbed comprising FlexRIC and a commercial RAN emulator, demonstrating effective detection rates, low latency overheads, and practical integration feasibility. Results indicate that the proposed safeguards can operate within near-RT time constraints while significantly improving protection against runtime attacks, introducing less than 80 ms overhead for a network with 500 User Equipment (UEs). Overall, this work lays the foundation for deployable, layered, and policy-driven runtime security architectures for the near-RT RIC control loop in Open RAN, and provides an extensible framework into which future mitigation policies and threat-specific modules can be integrated.

cs.CR↗

arXiv2025

KPI Poisoning: An Attack in Open RAN Near Real-Time Control Loop

Open Radio Access Network (Open RAN) is a new paradigm to provide fundamental features for supporting next-generation mobile networks. Disaggregation, virtualisation, closed-loop data-driven control, and open interfaces bring flexibility and interoperability to the network deployment. However, these features also create a new surface for security threats. In this paper, we introduce Key Performance Indicators (KPIs) poisoning attack in Near Real-Time control loops as a new form of threat that can have significant effects on the Open RAN functionality. This threat can arise from traffic spoofing on the E2 interface or compromised E2 nodes. The role of KPIs is explored in the use cases of Near Real-Time control loops. Then, the potential impacts of the attack are analysed. An ML-based approach is proposed to detect poisoned KPI values before using them in control loops. Emulations are conducted to generate KPI reports and inject anomalies into the values. A Long Short-Term Memory (LSTM) neural network model is used to detect anomalies. The results show that more amplified injected values are more accessible to detect, and using more report sequences leads to better performance in anomaly detection, with detection rates improving from 62% to 99%.

cs.NI↗