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Contenido proporcionado por Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
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Improving Analytics Using Enriched Network Flow Data

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Contenido proporcionado por Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

Classic tool suites that are used to process network flow records deal with very limited detail on the network connections they summarize. These tools limit detail for several reasons: (1) to maintain long-baseline data, (2) to focus on security-indicative data fields, and (3) to support data collection across large or complex infrastructures. However, a consequence of this limited detail is that analysis results based on this data provide information about indications of behavior rather than information that accurately identifies behavior with high confidence. In this webcast, Tim Shimeall and Katherine Prevost discuss how to use IPFIX-formatted data with detail derived from deep packet inspection (DPI) to provide increased confidence in identifying behavior.

  continue reading

148 episodios

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Manage episode 361742674 series 1264075
Contenido proporcionado por Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

Classic tool suites that are used to process network flow records deal with very limited detail on the network connections they summarize. These tools limit detail for several reasons: (1) to maintain long-baseline data, (2) to focus on security-indicative data fields, and (3) to support data collection across large or complex infrastructures. However, a consequence of this limited detail is that analysis results based on this data provide information about indications of behavior rather than information that accurately identifies behavior with high confidence. In this webcast, Tim Shimeall and Katherine Prevost discuss how to use IPFIX-formatted data with detail derived from deep packet inspection (DPI) to provide increased confidence in identifying behavior.

  continue reading

148 episodios

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