As part of the ENSURE-6G Staff Exchange Programme, Farah Abed Zadeh from University College Dublin (UCD), Ireland, is undertaking a research secondment at Montimage, France. This secondment contributes directly to ENSURE-6G Task 2.1, which focuses on the design of advanced security solutions for future 6G networks. Farah’s research background in AI/ML-based security mechanisms enabled cooperation with Montimage researchers, particularly in the area of network monitoring and attack detection, reinforcing ENSURE-6G’s objective of developing trustworthy and data-driven security frameworks.
Research Focus: RRC Signalling Storm Attacks in 5G O-RAN
Currently, Farah’s research focuses on the implementation and analysis of RRC Signalling Storm attacks, a critical threat capable of overloading radio access networks by abusing control-plane procedures. These attacks were implemented using Netslab’s 5G O-RAN testbed, enabling realistic experimentation under near-operational conditions. Both radio-level signals and network-level traffic were collected during the experiments. Network traffic was captured using Wireshark, generating .pcap files that are later converted into structured tabular datasets suitable for AI/ML model training and evaluation.
Synergy with Montimage Monitoring Tool (MMT)
A key outcome of the secondment was the strong alignment between the collected datasets and the capabilities of the Montimage Monitoring Tool (MMT). MMT is a comprehensive monitoring and analytics platform that supports data capture, filtering, storage, traffic analysis, and event correlation across network, flow, application, and user levels.
Through both real-time and historical views, MMT facilitates:
- network performance monitoring,
- operational troubleshooting, and
- detection of security incidents using advanced rule-based correlation.
MMT plays an important role in ensuring data quality and richness, supporting feature engineering, validation of extracted network features, and data analysis. These capabilities are essential for building robust AI/ML-based intrusion detection models.
Next Steps and Reflections
The collected datasets and monitoring insights form a strong foundation for the next phase, which involves training and evaluating machine-learning models for the detection of RRC Signalling Storm attacks.
Beyond technical results, this work demonstrates the importance of high-quality monitoring and data engineering as prerequisites for reliable AI-driven security in 6G systems. By combining the expertise of different researchers and their technological insights, ENSURE-6G moves closer to deployable, trustworthy security solutions.
Farah would like to express her sincere gratitude to the Montimage team for their warm welcome, technical expertise, and collaborative spirit, and looks forward to sharing the forthcoming results of this work with the wider ENSURE-6G community.
