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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Darlan Noetzold, Jorge Luis Victória Barbosa, F. de Paz Santana & Valderi R. Q. Leithardt (2026). A Comprehensive Dataset for Context-Aware Security Monitoring for Anomaly Detection.
Exportar Referência (IEEE) 
D. Noetzold et al.,  "A Comprehensive Dataset for Context-Aware Security Monitoring for Anomaly Detection",, 2026
Exportar BibTeX 
@techreport{noetzold2026_1770116953117,
	author = "Darlan Noetzold and Jorge Luis Victória Barbosa and F. de Paz Santana and Valderi R. Q. Leithardt",
	title = "A Comprehensive Dataset for Context-Aware Security Monitoring for Anomaly Detection",
	year = "2026",
	number = "",
	institution = "Mendeley Data",
	address = "",
	url = "https://data.mendeley.com/datasets/crgh2ynmzv/2"
}
Exportar RIS 
TY  - RPRT
TI  - A Comprehensive Dataset for Context-Aware Security Monitoring for Anomaly Detection
AU  - Darlan Noetzold
AU  - Jorge Luis Victória Barbosa
AU  - F. de Paz Santana
AU  - Valderi R. Q. Leithardt
PY  - 2026
DO  - 10.17632/crgh2ynmzv.2
UR  - https://data.mendeley.com/datasets/crgh2ynmzv/2
AB  - DATASET
This article describes a high-dimensional dataset designed for context-aware security monitoring and anomaly detection in digital transaction systems. The data encompasses 112 features, including transactional metadata, user behavioral patterns, and advanced technical telemetry (IP reputation, VPN detection, device fingerprinting, network security indicators). A unique aspect of this dataset is the integration of Large Language Model (LLM) outputs, providing risk scores and natural language reasoning for contextual security assessment. This dataset is particularly valuable for training machine learning models that require a hybrid approach, combining traditional tabular data with AI-generated contextual insights to identify security threats such as account takeover, suspicious behavioral patterns, and social engineering attacks across multiple digital channels.
ER  -