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DTSTART:20260308T030000
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DTSTART:20251102T010000
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DTSTAMP:20251113T035431Z
UID:8F69F0A5-B6C3-4461-9D04-FEED659D1764
DTSTART;TZID=America/New_York:20251113T120000
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DESCRIPTION:Join us for the sixth session of the exciting webinar series on
  &quot;New Frontiers in Signal Processing in 6G Wireless Networks&quot;\, a collabor
 ation between IEEE Signal Processing\, IEEE Communications Society chapter
 s in Ottawa\, and IEEE ComSoC Young Professionals.\n\nRegister now and sta
 y tuned for updates on upcoming speakers and topics!\n\nSpeaker(s): Prof. 
 Georges Kaddoum\n\nAgenda: \nThe integration of machine learning (ML) acro
 ss different layers of modern wireless communication networks has signific
 antly enhanced efficiency\, adaptability\, and automation. However\, thisa
 dvancement has also introduced new security vulnerabilities. Adversarial a
 ttacks\, in particular\, exploit weaknesses in ML models to misclassify si
 gnals\, degrade network performance\, and disrupt critical operations.\n\n
 This talk examines the growing threat of adversarial attacks that target M
 L-based signal processing\, resource allocation\, and security protocols i
 n wireless networks. It discusses the main challenges in protecting ML-dri
 ven systems\, reviews recent progress in defense techniques such as Bayesi
 an learning\, robust training\, and adaptive protection methods\, and outl
 ines open research directions for developing more resilient and trustworth
 y intelligent communication frameworks.\n\nThe presentation provides an ov
 erview of the adversarial landscape\, effective defense strategies\, and f
 uture research opportunities at the intersection of machine learning and w
 ireless network security.\n\nVirtual: https://events.vtools.ieee.org/m/513
 118
LOCATION:Virtual: https://events.vtools.ieee.org/m/513118
ORGANIZER:monireh.vamegh@ieee.org
SEQUENCE:42
SUMMARY:Adversarial Threats in ML-Driven Wireless Networks: Challenges\, De
 fenses\, and the Road Ahead
URL;VALUE=URI:https://events.vtools.ieee.org/m/513118
X-ALT-DESC:Description: &lt;br /&gt;&lt;p style=&quot;margin: 5.25pt 0in .0001pt 0in\;&quot;&gt;&lt;
 em&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;Join us for the sixth 
 session of the exciting webinar series on &quot;New Frontiers in Signal Process
 ing in 6G Wireless Networks&quot;\, a collaboration between IEEE Signal Process
 ing\, IEEE Communications Society chapters in Ottawa\, and IEEE ComSoC You
 ng Professionals.&amp;nbsp\;&lt;/span&gt;&lt;/em&gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 5.25pt 0in .00
 01pt 0in\;&quot;&gt;&lt;em&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;Register 
 now and stay tuned for updates on upcoming speakers and topics!&lt;/span&gt;&lt;/em
 &gt;&lt;/p&gt;\n&lt;p style=&quot;margin: 5.25pt 0in .0001pt 0in\;&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p style=&quot;
 margin: 5.25pt 0in .0001pt 0in\;&quot;&gt;&lt;em&gt;&lt;span style=&quot;font-size: 13.5pt\; col
 or: black\;&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/media/disp
 lay/4fd0c48c-78b5-45f7-9cee-68b843fff0aa&quot;&gt;&lt;/span&gt;&lt;/em&gt;&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agen
 da: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot;&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;The integ
 ration of machine learning (ML) across different layers of modern wireless
  communication networks has significant&lt;span style=&quot;font-family: -apple-sy
 stem\, BlinkMacSystemFont\, &#39;Segoe UI&#39;\, Roboto\, Oxygen\, Ubuntu\, Cantar
 ell\, &#39;Open Sans&#39;\, &#39;Helvetica Neue&#39;\, sans-serif\;&quot;&gt;ly enhanced efficienc
 y\, adaptability\, and automation. However\, this&lt;/span&gt;&lt;span style=&quot;font-
 family: -apple-system\, BlinkMacSystemFont\, &#39;Segoe UI&#39;\, Roboto\, Oxygen\
 , Ubuntu\, Cantarell\, &#39;Open Sans&#39;\, &#39;Helvetica Neue&#39;\, sans-serif\;&quot;&gt;adva
 ncement has also introduced new security vulnerabilities. Adversarial atta
 cks\, in particular\, exploit weaknesses in ML models to misclassify signa
 ls\, degrade network performance\, and disrupt critical operations.&lt;/span&gt;
 &lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot;&gt;This talk examines the growing threat of advers
 arial attacks that target ML-based signal processing\, resource allocation
 \, and security protocols in wireless networks. It discusses the main chal
 lenges in protecting ML-driven systems\, reviews recent progress in defens
 e techniques such as Bayesian learning\, robust training\, and adaptive pr
 otection methods\, and outlines open research directions for developing mo
 re resilient and trustworthy intelligent communication frameworks.&lt;/p&gt;\n&lt;p
  class=&quot;MsoNormal&quot;&gt;The presentation provides an overview of the adversaria
 l landscape\, effective defense strategies\, and future research opportuni
 ties at the intersection of machine learning and wireless network security
 .&lt;/p&gt;
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