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DTSTART:20260329T020000
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DTSTAMP:20260716T123302Z
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DTSTART;TZID=Europe/Lisbon:20260713T143000
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DESCRIPTION:Abstract: The growing adoption of the Internet of Medical Thing
 s (IoMT) has improved healthcare through real-time data exchange and devic
 e interconnectivity. However\, this rapid adoption also increases the atta
 ck surface\, exposing critical systems to cyber threats. Current intrusion
  detection systems (IDS) are typically centralized and computationally int
 ensive\, making them unsuitable for resource-constrained IoMT environments
 . This research proposes a hierarchical and modular detection framework th
 at distributes anomaly detection to the network edge while delegating clas
 sification tasks to cloud-based models\, enabling early threat identificat
 ion without compromising network performance by leveraging edge interactio
 ns.\n\nde\n\nCo-sponsored by: Instituto de Telecomunicações e Universida
 de da Beira Interior\, Covilhã\n\nSpeaker(s): Yeritza Gómez\, \n\nAgenda
 : \n02:30pm Yeritza\, Gómez Martínez\, A Hierarchical Framework for Anom
 aly Detection and Attack Classification in the Internet of Medical Things 
 (IoMT)\n\n03:15pm Q & A\n\nRoom: 08.01\, Bldg: 8\, Fac. de Engenharia\, In
 stituto Telec -DEM\, Universidade da Beira Interior\, Covilhã\, Castelo B
 ranco\, Portugal\, Centro\, Portugal\, 6201-001\, Virtual: https://events.
 vtools.ieee.org/m/567038
LOCATION:Room: 08.01\, Bldg: 8\, Fac. de Engenharia\, Instituto Telec -DEM\
 , Universidade da Beira Interior\, Covilhã\, Castelo Branco\, Portugal\, 
 Centro\, Portugal\, 6201-001\, Virtual: https://events.vtools.ieee.org/m/5
 67038
ORGANIZER:fjv@ubi.pt
SEQUENCE:22
SUMMARY:Talk on "A Hierarchical Framework for Anomaly Detection and Attack 
 Classification in the Internet of Medical Things (IoMT)"
URL;VALUE=URI:https://events.vtools.ieee.org/m/567038
X-ALT-DESC:Description: <br /><p class="MsoNormal"><strong>Abstract:&nbsp\;
 </strong>The growing adoption of the Internet of Medical Things (IoMT) has
  improved healthcare through real-time data exchange and device interconne
 ctivity. However\, this rapid adoption also increases the attack surface\,
  exposing critical systems to cyber threats. Current intrusion detection s
 ystems (IDS) are typically centralized and computationally intensive\, mak
 ing them unsuitable for resource-constrained IoMT environments. This resea
 rch proposes a hierarchical and modular detection framework that distribut
 es anomaly detection to the network edge while delegating classification t
 asks to cloud-based models\, enabling early threat identification without 
 compromising network performance by leveraging edge interactions.</p>\n<p 
 class="MsoNormal">de&nbsp\;</p>\n<p class="MsoNormal"><img src="https://ev
 ents.vtools.ieee.org/vtools_ui/media/display/01966601-f673-475f-8a1e-a21a8
 b19455f" width="879" height="148"></p><br /><br />Agenda: <br /><p>02:30pm
  Yeritza\, G&oacute\;mez Mart&iacute\;nez\, A Hierarchical Framework for A
 nomaly Detection and Attack Classification in the Internet of Medical Thin
 gs (IoMT)</p>\n<p>03:15pm Q &amp\; A</p>
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