arXiv subjects

Hamed Alimohammadi

Publications and source records attributed to Hamed Alimohammadi.

4 recordsLinked to original sources

arXiv2026

rApp/xApp Attestation: A New Security Use Case for O-RAN

The disaggregation and softwarization introduced by the Open Radio Access Network (O-RAN) architecture enable multi-vendor innovation but also expose the RAN Intelligent Controller (RIC) ecosystem to new runtime security risks. Existing O-RAN specifications define strong safeguards for onboarding, authentication, identity management, and secure communication; however, they do not provide a concrete mechanism for verifying whether deployed rApps and xApps remain in their intended, untampered state during operation. This paper introduces rApp/xApp attestation as a RIC-native O-RAN security use case for runtime integrity verification. Rather than proposing a new cryptographic protocol, the work defines how existing integrity verification techniques can be integrated into O-RAN through attestation modules, attestation agents, RIC application interfaces, and SMO-driven policy coordination. We map the use case to relevant O-RAN Alliance working groups, identify required standardization extensions, and demonstrate feasibility through a lightweight hash-based prototype implemented on the Near-RT RIC platform. Experimental results show attestation latencies below 40 ms across multiple cryptographic hash functions, indicating that runtime attestation can be performed without disrupting time-sensitive RIC operations when appropriately scheduled. Finally, we discuss remaining technical and standardization challenges, including trusted verification, known-good runtime states, scalability, mitigation policies, and future hybrid attestation mechanisms.

cs.CR↗

arXiv2026

Solving the Post-Quantum Control Plane Bottleneck: Energy-Aware Cryptographic Scheduling in Open RAN

The Open Radio Access Network (O-RAN) offers flexibility and innovation but introduces unique security vulnerabilities, particularly from cryptographically relevant quantum computers. While Post-Quantum Cryptography (PQC) is the primary scalable defence, its computationally intensive handshakes create a significant bottleneck for the RAN control plane, posing sustainability challenges. This paper proposes an energy-aware framework to solve this PQC bottleneck, ensuring quantum resilience without sacrificing operational energy efficiency. The system employs an O-RAN aligned split: a Crypto Policy rApp residing in the Non-Real-Time (Non-RT) RIC defines the strategic security envelope (including PQC suites), while a Security Operations Scheduling (SOS) xApp in the Near-RT RIC converts these into tactical timing and placement intents. Cryptographic enforcement remains at standards-compliant endpoints: the Open Fronthaul utilizes Media Access Control Security (MACsec) at the O-DU/O-RU, while the xhaul (midhaul and backhaul) utilizes IP Security (IPsec) at tunnel terminators. The SOS xApp reduces PQC overhead by batching non-urgent handshakes, prioritizing session resumption, and selecting parameters that meet slice SLAs while minimizing joules per secure connection. We evaluate the architecture via a Discrete-Event Simulation (DES) using 3GPP-aligned traffic profiles and verified hardware benchmarks from literature. Results show that intelligent scheduling can reduce per-handshake energy by approximately 60 percent without violating slice latency targets.

cs.CR↗

arXiv2026

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↗