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Innehåll tillhandahållet av Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
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Improving Analytics Using Enriched Network Flow Data

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Manage episode 361742674 series 1264075
Innehåll tillhandahållet av Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.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

151 episoder

Artwork
iconDela
 
Manage episode 361742674 series 1264075
Innehåll tillhandahållet av Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.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

151 episoder

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