BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250608T022123Z UID:A2975B69-D4B7-440B-B7A8-0FBC5ACB625E DTSTART;TZID=America/Chicago:20250604T110000 DTEND;TZID=America/Chicago:20250604T120000 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\nSpeaker(s): Dr. Ljiljana Trajkovic\, \n\nVir tual: https://events.vtools.ieee.org/m/487731 LOCATION:Virtual: https://events.vtools.ieee.org/m/487731 ORGANIZER:manishaguduri@ieee.org SEQUENCE:22 SUMMARY:IEEE Lafayette Section - Summer Digital Dialogues :Data Mining And Machine Learning For Analysis Of Network Traffic. URL;VALUE=URI:https://events.vtools.ieee.org/m/487731 X-ALT-DESC:Description: <br /><p>Collection and analysis of data from deplo yed networks is essential for understanding modern communication networks. Data mining and statistical analysis of network data are often employed t o determine traffic loads\, analyze patterns of users' behavior\, and pred ict future network traffic while various machine learning techniques prove d valuable for predicting anomalous traffic behavior. In described case st udies\, traffic traces collected from various deployed networks and the In ternet are used to characterize and model network traffic\, analyze Intern et topologies\, and classify network anomalies.&nbsp\;</p> END:VEVENT END:VCALENDAR