NS-MFM-DGA: Anomaly Detection Data for Software-Defined Industrial Cyber-Physical Systems

86.7 KB of data supporting a neural-symbolic dynamic graph framework for real-time anomaly detection. The dataset, authored by Senlin Jiang, was last updated on May 30, 2026, and is shared under a CC-BY-4.0 license on figshare. It is associated with a model designed to address cross-modal attack evidence and dynamic topology changes in industrial systems.

Use Cases

• Training real-time anomaly detection models based on dynamic graph structures.
• Benchmarking multimodal security models against cross-modal attack evidence.
• Studying the impact of network reconfiguration on system topology in SD-ICPS.

Strengths

• Data is shared under a permissive CC-BY-4.0 license.
• The associated framework is specifically designed for real-time detection in dynamic industrial networks.
• The description identifies a clear research gap in handling cross-modal attacks and static graph models.

Limitations

• The dataset is very small at 86.7 KB, suggesting limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale training.

Provenance

Source

figshare, author Senlin Jiang.

Collection Method

Likely generated or collected as part of research on the NS-MFM-DGA model.

Freshness

Last updated 2026-05-30 07:44:37; freshness should be verified.