Ismael Gómez-Talal

Dafne Lozano-Paredes, Luis Bote-Curiel, Juan Ramón Feijóo-Martínez et al.

The IEC 61850 Generic Object-Oriented Substation Event (GOOSE) protocol plays a critical role in real-time protection and automation of digital substations, yet its lack of native security mechanisms can expose power systems to sophisticated cyberattacks. Traditional rule-based and supervised intrusion detection techniques struggle to detect protocol-compliant and zero-day attacks under significant class imbalance and limited availability of labeled data. This paper proposes an explainable, unsupervised multi-view anomaly detection framework for IEC 61850 GOOSE networks that explicitly separates semantic integrity and temporal availability. The approach employs asymmetric autoencoders trained only on real operational GOOSE traffic to learn distinct latent representations of sequence-based protocol semantics and timing-related transmission dynamics in normal traffic. Anomaly detection is implemented using reconstruction errors mixed with statistically grounded thresholds, enabling robust detection without specified attack types. Feature-level reconstruction analysis provides intrinsic explainability by directly linking detection outcomes to IEC 61850 protocol characteristics. The proposed framework is evaluated using real substation traffic for training and a public dataset containing normal traffic and message suppression, data manipulation, and denial-of-service attacks for testing. Experimental results show attack detection rates above 99% with false positives remaining below 5% of total traffic, demonstrating strong generalization across environments and effective operation under extreme class imbalance and interpretable anomaly attribution.

Enrique Feito-Casares, Ismael Gómez-Talal, José-Luis Rojo-Álvarez

This data article introduces a comprehensive, high-resolution honeynet dataset designed to support standalone analyses of global cyberattack behaviors. Collected over a continuous 72-hour window (June 9 to 11, 2025) on Microsoft Azure, the dataset comprises 132,425 individual attack events captured by three honeypots (Cowrie, Dionaea, and SentryPeer) deployed across four geographically dispersed virtual machines. Each event record includes enriched metadata (UTC timestamps, source/destination IPs, autonomous system and organizational mappings, geolocation coordinates, targeted ports, and honeypot identifiers alongside derived temporal features and standardized protocol classifications). We provide actionable guidance for researchers seeking to leverage this dataset in anomaly detection, protocol-misuse studies, threat intelligence, and defensive policy design. Descriptive statistics highlight significant skew: 2,438 unique source IPs span 95 countries, yet the top 1% of IPs account for 1% of all events, and three protocols dominate: Session Initiation Protocol (SIP), Telnet, Server Message Block (SMB). Temporal analysis uncovers pronounced rush-hour peaks at 07:00 and 23:00 UTC, interspersed with maintenance-induced gaps that reveal operational blind spots. Geospatial mapping further underscores platform-specific biases: SentryPeer captures concentrated SIP floods in North America and Southeast Asia, Cowrie logs Telnet/SSH scans predominantly from Western Europe and the U.S., and Dionaea records SMB exploits around European nodes. By combining fine-grained temporal resolution with rich, contextual geolocation and protocol metadata, this standalone dataset aims to empower reproducible, cloud-scale investigations into evolving cyber threats. Accompanying analysis code and data access details are provided.