Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks
Machine learning (ML)-based intrusion detection systems (IDSs) are increasingly used to monitor encrypted industrial communication. However, their behavior under realistic private 5G operating conditions remains insufficiently understood. This paper investigates the impact of benign connectivity variations on ML-based IDSs for encrypted Open Platform Communications Unified Architecture (OPC UA) traffic in industrial private 5G networks. Experimental results show that legitimate connectivity even
Record details
Published: 10 July 2026
Source: arXiv cs.CR (AI security)
Category: Research
Topics: Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (10 July 2026), “Impact of Benign Connectivity Variations on Intrusion Detection for Encrypted OPC UA Traffic in Industrial Private 5G Networks,” evidence record 10173, https://ethics.ai/record/10173 (originally published by arXiv cs.CR (AI security)).
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