← 研踪 PaperTrailarXiv:2611.13942 · v1
Data4Cyber: Labeled Cyber-Physical Datasets for Distribution-Grid Management Systems Under Representative OT Attacks
首次提交 2026-11-10 19:29
版本更新 2026-11-10 19:29
雷达观测 2026-11-11 11:04
摘要
Cyberattacks against power systems increasingly exploit industrial communication and control dependencies arising from distributed energy resources (DER), remote management, and market-based incentives. Progress on intrusion detection is constrained by a lack of public datasets that align operational technology (OT) network telemetry with physical measurements and control context. Data4Cyber is a labeled cyber-physical dataset recorded in a laboratory distribution management system (DMS) for self-consumption optimization with photovoltaic generation, battery storage, controllable loads, and substation metering. It captures synchronized process measurements, controller context, price incentives, and packet captures (Modbus/TCP and MQTT) across one benign baseline and six attack scenarios spanning protocol-semantic Modbus manipulation, man-in-the-middle (MitM) false-data injection, and MQTT price manipulation. Its fixed 1 s time base, phase-resolved labels, and state-oriented representation support temporally blocked stateful-detector evaluation. Baseline results show that high recall alone is insufficient: several detectors achieve sensitivity through alarm saturation. Context-driven attacks require process-aware interpretation rather than single-channel thresholding.
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v1 · 2026-11-10 19:29 · 最新观测版本
Data4Cyber: Labeled Cyber-Physical Datasets for Distribution-Grid Management Systems Under Representative OT Attacks
Cyberattacks against power systems increasingly exploit industrial communication and control dependencies arising from distributed energy resources (DER), remote management, and market-based incentives. Progress on intrusion detection is constrained by a lack of public datasets that align operational technology (OT) network telemetry with physical measurements and control context. Data4Cyber is a labeled cyber-physical dataset recorded in a laboratory distribution management system (DMS) for self-consumption optimization with photovoltaic generation, battery storage, controllable loads, and substation metering. It captures synchronized process measurements, controller context, price incentives, and packet captures (Modbus/TCP and MQTT) across one benign baseline and six attack scenarios spanning protocol-semantic Modbus manipulation, man-in-the-middle (MitM) false-data injection, and MQTT price manipulation. Its fixed 1 s time base, phase-resolved labels, and state-oriented representation support temporally blocked stateful-detector evaluation. Baseline results show that high recall alone is insufficient: several detectors achieve sensitivity through alarm saturation. Context-driven attacks require process-aware interpretation rather than single-channel thresholding.
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