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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Asia/Jakarta
BEGIN:STANDARD
DTSTART:19631231T233000
TZOFFSETFROM:+0730
TZOFFSETTO:+0700
TZNAME:WIB
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BEGIN:VEVENT
DTSTAMP:20210426T061157Z
UID:0AFD2615-ACD1-4081-991A-61C2E58BD776
DTSTART;TZID=Asia/Jakarta:20210426T110000
DTEND;TZID=Asia/Jakarta:20210426T130000
DESCRIPTION:Collection and analysis of data from deployed networks is essen
 tial for understanding modern communication networks. Data mining and stat
 istical analysis of network data are often employed to determine traffic l
 oads\, analyze patterns of users' behavior\, and predict future network tr
 affic while various machine learning techniques proved valuable for predic
 ting anomalous traffic behavior. In described case studies\, traffic trace
 s collected from various deployed networks and the Internet are used to ch
 aracterize and model network traffic\, analyze Internet topologies\, and c
 lassify network anomalies.\n\nCo-sponsored by: School of Electrical Engine
 ering and Informatics\, Institut Teknologi Bandung\, Indonesia\n\nSpeaker(
 s): Ljiljana Trajkovic\, \n\nVirtual: https://events.vtools.ieee.org/m/268
 778
LOCATION:Virtual: https://events.vtools.ieee.org/m/268778
ORGANIZER:tutun@itb.ac.id
SEQUENCE:5
SUMMARY:Distinguished Lecture on Data Mining and Machine Learning for Analy
 sis of Network Traffic
URL;VALUE=URI:https://events.vtools.ieee.org/m/268778
X-ALT-DESC:Description: <br /><p>&nbsp\;</p>\n<p>Collection and analysis of
  data from deployed networks is essential for understanding modern communi
 cation networks. Data mining and statistical analysis of network data are 
 often employed to determine traffic loads\, analyze patterns of users' beh
 avior\, and predict future network traffic while various machine learning 
 techniques proved valuable for predicting anomalous traffic behavior. In d
 escribed case studies\, traffic traces collected from various deployed net
 works and the Internet are used to characterize and model network traffic\
 , analyze Internet topologies\, and classify network anomalies.</p>
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