BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//cfp.pass-the-salt.org//pts2022//speaker//EPHKZY
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-pts2022-ZFF89E@cfp.pass-the-salt.org
DTSTART;TZID=CET:20220705T111500
DTEND;TZID=CET:20220705T115000
DESCRIPTION:An RF Signal is an element that a human cannot see nor hear\, b
 ut could be measured with many means today. Particularly\, the Software-De
 fined Radio allows even people with a low budget to observe radio frequenc
 ies in real-time\, and so make they capture different types of communicati
 ons: AM/FM\, Mobile & LPWAN communications\, etc. There are many ways to c
 lassify all the technologies depending on the used frequency\, used bandwi
 dth\, duty cycle\, and patterns\, but it is sometimes hard and/or time-con
 suming to recognize these technologies.\nTo resolve these types of challen
 ges\, we thought about using Machine & Deep Learning tools to optimize our
  classification\, and we wanted to share with you our successes\, mistakes
 \, and other feedback. In addition to proper classification\, RF emanation
 s are also permanent in the air\, and we will see that the same techniques
  can be applied to match harmonics\, but also for side-channel attacks as 
 well.
DTSTAMP:20260515T163944Z
LOCATION:Amphitheater
SUMMARY:Use of Machine and Deep Learning on RF Signals - Sébastien Dudek
URL:https://cfp.pass-the-salt.org/pts2022/talk/ZFF89E/
END:VEVENT
END:VCALENDAR