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SUMMARY: Flow-based Encrypted Traffic Analysis - Daniel Poliakov\, Brno Un
 iversity of Technology
DTSTART:20241128T150000Z
DTEND:20241128T160000Z
UID:TALK224554@talks.cam.ac.uk
CONTACT:Richard Mortier
DESCRIPTION:Network traffic classification is essential for ensuring Quali
 ty of Service\, enforcing policies\, or identifying malware\, botnets\, an
 d their corresponding command-and-control servers. However\, the growing c
 omplexity of encrypted traffic\, due to the prevalence of TLS\, QUIC\, and
  DNS over HTTPS\, presents significant challenges to traditional traffic a
 nalysis techniques. This talk will discuss advancements in machine learnin
 g for processing encrypted network data at the flow level in high-speed co
 mputer networks\, outlining methods that leverage statistical information 
 from metadata such as packet lengths and inter-arrival times. The talk wil
 l cover different statistical data representations on input\, modelling ar
 chitectures\, and open problems in flow embedding extraction\, contrastive
  learning\, and model transfer.\n\nBio: Daniel Poliakov is a Ph.D. student
  at Brno University of Technology with current research interests in data 
 modelling\, neural networks\, and representation learning for applications
  in network traffic monitoring and security. Prior to starting his Ph.D.\,
  he focused on automated malware analysis\, network security research\, an
 d threat intelligence.\n\nJoin online: https://teams.microsoft.com/l/meetu
 p-join/19:meeting_ZDhlNGE0YTAtMDY5Mi00NGZhLTljOGEtM2ZiNGEyY2Y4Nzc2@thread.
 v2/0?context=%7B%22Tid%22:%2249a50445-bdfa-4b79-ade3-547b4f3986e9%22\,%22O
 id%22:%22c74ff4ca-98fe-4b28-9889-e119acc12f30%22%7D
LOCATION:Computer Lab\, FW11
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