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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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There have been five major extinction events throughout geological history, resulting in the elimination of biodiversity, followed by a rebound of species that adapted to the new conditions. In the current geological epoch, the Holocene, there is a sixth extinction event in progress. This mass extinction has been attributed to human activities and is thus provisionally called the Anthropocene. In 2019 the human population reached 7.7 billion people and is projected to comprise 10 billion by...
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Intelligence01:27

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The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
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Protein Networks02:26

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Network Covalent Solids02:18

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Measures of Intelligence01:29

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Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
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Detección de URL maliciosas impulsada por metadatos utilizando RoBERTa large e inteligencia de amenazas de red

Lina Chen1, Liang Meng2

  • 1Guangxi Power Grid Co. Ltd., Nanning, 530022, China.

Scientific reports
|January 29, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un enfoque novedoso que utiliza transformadores RoBERTa-Large para la detección avanzada de URL maliciosas, logrando una precisión del 98 %. Este método supera significativamente a los modelos tradicionales de aprendizaje automático y aprendizaje profundo en la identificación de amenazas de phishing y malware.

Palabras clave:
Inteligencia artificialCiberseguridadAprendizaje profundoDetección de URL maliciosasRoBERTaTransformadores

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Área de la Ciencia:

  • Ciberseguridad
  • Inteligencia artificial
  • Procesamiento del lenguaje natural

Sus antecedentes:

  • Las URL maliciosas son un vector principal para ciberataques como el phishing y la distribución de malware.
  • Los modelos existentes de aprendizaje automático (ML) y aprendizaje profundo (DL) luchan contra las manipulaciones adversarias novedosas.
  • Los modelos secuenciales capturan patrones a nivel de carácter pero carecen de robustez contra amenazas sofisticadas.

Objetivo del estudio:

  • Desarrollar un método robusto y preciso para detectar URL maliciosas.
  • Aprovechar los modelos de lenguaje grandes de última generación para mejorar la seguridad.
  • Mejorar la interpretabilidad de los modelos de detección de URL maliciosas.

Principales métodos:

  • Aplicación de transformadores RoBERTa-Large, un modelo de lenguaje grande de doble mecanismo.
  • Integración de incrustaciones de subpalabras contextualizadas con señales de metadatos a través de capas de atención.
  • Ajuste fino del modelo en un conjunto de datos equilibrado de URL benignas, de defacement, de phishing y de malware.

Principales resultados:

  • Se logró una precisión general del 98 % en la detección de URL maliciosas.
  • Superó sustancialmente a los modelos de ML y DL existentes.
  • El análisis SHAP y LIME confirmó características clave (longitud de la URL, profundidad de las barras, entropía) e identificó anomalías léxicas sutiles a través de cabezales de atención.

Conclusiones:

  • La integración de la atención de metadatos con modelos de lenguaje enmascarados ofrece un rendimiento de última generación para la detección de URL maliciosas.
  • El método propuesto proporciona una toma de decisiones transparente para aplicaciones del mundo real.
  • Los transformadores RoBERTa-Large representan un avance significativo en la detección de amenazas de ciberseguridad.