Nathan Shone | Searcharxiv

arXiv2026

Towards Quantum-Safe O-RAN -- Experimental Evaluation of ML-KEM-Based IPsec on the E2 Interface

As Open Radio Access Network (O-RAN) deployments expand and adversaries adopt 'store-now, decrypt-later' strategies, operators need empirical data on the cost of migrating critical control interfaces to post-quantum cryptography (PQC). This paper experimentally evaluates the impact of integrating a NIST-aligned module-lattice KEM (ML-KEM, CRYSTALS-Kyber) into IKEv2/IPsec protecting the E2 interface between the 5G Node B (gNB) and the Near-Real-Time RAN Intelligent Controller (Near-RT RIC). Using an open-source testbed built from srsRAN, Open5GS, FlexRIC and strongSwan (with liboqs), we compare three configurations: no IPsec, classical ECDH-based IPsec, and ML-KEM-based IPsec. The study focuses on IPsec tunnel-setup latency and the runtime behaviour of Near-RT RIC xApps under realistic signalling workloads. Results from repeated, automated runs show that ML-KEM integration adds a small overhead to tunnel establishment, which is approximately 3~5 ms in comparison to classical IPsec, while xApp operation and RIC control loops remain stable in our experiments. These findings indicate that ML-KEM based IPsec on the E2 interface is practically feasible and inform quantum-safe migration strategies for O-RAN deployments.

cs.CR↗

arXiv2025

A Collaborative Intrusion Detection System Using Snort IDS Nodes

Intrusion Detection Systems (IDSs) are integral to safeguarding networks by detecting and responding to threats from malicious traffic or compromised devices. However, standalone IDS deployments often fall short when addressing the increasing complexity and scale of modern cyberattacks. This paper proposes a Collaborative Intrusion Detection System (CIDS) that leverages Snort, an open-source network intrusion detection system, to enhance detection accuracy and reduce false positives. The proposed architecture connects multiple Snort IDS nodes to a centralised node and integrates with a Security Information and Event Management (SIEM) platform to facilitate real-time data sharing, correlation, and analysis. The CIDS design includes a scalable configuration of Snort sensors, a centralised database for log storage, and LogScale SIEM for advanced analytics and visualisation. By aggregating and analysing intrusion data from multiple nodes, the system enables improved detection of distributed and sophisticated attack patterns that standalone IDSs may miss. Performance evaluation against simulated attacks, including Nmap port scans and ICMP flood attacks, demonstrates our CIDS's ability to efficiently process large-scale network traffic, detect threats with higher accuracy, and reduce alert fatigue. This paper highlights the potential of CIDS in modern network environments and explores future enhancements, such as integrating machine learning for advanced threat detection and creating public datasets to support collaborative research. The proposed CIDS framework provides a promising foundation for building more resilient and adaptive network security systems.

cs.CR↗