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Kandungan disediakan oleh Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
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Improving Analytics Using Enriched Network Flow Data

1:02:25
 
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Manage episode 361742674 series 1264075
Kandungan disediakan oleh Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.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

174 episod

Artwork
iconKongsi
 
Manage episode 361742674 series 1264075
Kandungan disediakan oleh Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Carnegie Mellon University Software Engineering Institute and SEI Members of Technical Staff atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.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

174 episod

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