Robust Anomaly Detection in O-RAN: Leveraging LLMs against Data Manipulation Attacks
This addresses security vulnerabilities in 5G O-RAN networks for telecom operators, but it is incremental as it applies an existing method (LLMs) to a new domain-specific problem.
The paper tackles the problem of data manipulation attacks in O-RAN architectures, where malicious xApps use Unicode-wise alterations to bypass traditional ML-based anomaly detection systems, and demonstrates that LLM-based xApps achieve robust operational performance with low detection latency under 0.07 seconds, though detection accuracy needs improvement.
The introduction of 5G and the Open Radio Access Network (O-RAN) architecture has enabled more flexible and intelligent network deployments. However, the increased complexity and openness of these architectures also introduce novel security challenges, such as data manipulation attacks on the semi-standardised Shared Data Layer (SDL) within the O-RAN platform through malicious xApps. In particular, malicious xApps can exploit this vulnerability by introducing subtle Unicode-wise alterations (hypoglyphs) into the data that are being used by traditional machine learning (ML)-based anomaly detection methods. These Unicode-wise manipulations can potentially bypass detection and cause failures in anomaly detection systems based on traditional ML, such as AutoEncoders, which are unable to process hypoglyphed data without crashing. We investigate the use of Large Language Models (LLMs) for anomaly detection within the O-RAN architecture to address this challenge. We demonstrate that LLM-based xApps maintain robust operational performance and are capable of processing manipulated messages without crashing. While initial detection accuracy requires further improvements, our results highlight the robustness of LLMs to adversarial attacks such as hypoglyphs in input data. There is potential to use their adaptability through prompt engineering to further improve the accuracy, although this requires further research. Additionally, we show that LLMs achieve low detection latency (under 0.07 seconds), making them suitable for Near-Real-Time (Near-RT) RIC deployments.