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DTSTART:20210314T030000
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DTSTAMP:20210511T030241Z
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DTSTART;TZID=Canada/Mountain:20210505T120000
DTEND;TZID=Canada/Mountain:20210505T130000
DESCRIPTION:The field of cyber-physical security has evolved greatly over t
 he last decade especially in the context of critical infrastructures such 
 as the smart grid. The current challenges aim to address the increased sop
 histication of cyberattacks in the context of a more automated grid. Emerg
 ing polymorphic and stealthy attacks necessitate more coordinated and inte
 lligent approaches to mitigation. In addition to the typical defense-in-de
 pth paradigm\, more harmonized protection and resilience strategies are es
 sential. Development of next-generation tools for cyber-physical security 
 requires the adoption of effective models that are compatible with salient
  trends in smart grid infrastructure including Information Technology/Oper
 ational Technology (IT/OT) convergence. The resulting data-rich cyber-phys
 ical environment from IT/OT convergence suggests a strong need for greater
  data-driven modelling paradigms and analytics. In this talk\, we provide 
 examples of deep learning in the context of anomaly detection for the cybe
 r-physical protection of transmission protection systems. We then present 
 a brave new world of opportunities for smart grid cyber-physical security 
 using a data analytics-driven approach.\n\nAgenda: \n» 11:50 Webinar open
 \n\n» 12:00 IEEE Southern Alberta announcements\n\n» 12:05 Presentation 
 by Dr. Kundur\n\n» 12:45 Q&amp;A\n\n» 13:00 Webinar close\n\nVirtual: https:
 //events.vtools.ieee.org/m/266454
LOCATION:Virtual: https://events.vtools.ieee.org/m/266454
ORGANIZER:amir.kaboli@ieee.org
SEQUENCE:5
SUMMARY:Analytics-Driven Cyber-Physical Security for a Converged Smart Grid
URL;VALUE=URI:https://events.vtools.ieee.org/m/266454
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;The field of cyber-physical security has e
 volved greatly over the last decade especially in the context of critical 
 infrastructures such as the smart grid. The current challenges aim to addr
 ess the increased sophistication of cyberattacks in the context of a more 
 automated grid. Emerging polymorphic and stealthy attacks necessitate more
  coordinated and intelligent approaches to mitigation. In addition to the 
 typical defense-in-depth paradigm\, more harmonized protection and resilie
 nce strategies are essential. Development of next-generation tools for cyb
 er-physical security requires the adoption of effective models that are co
 mpatible with salient trends in smart grid infrastructure including Inform
 ation Technology/Operational Technology (IT/OT) convergence. The resulting
  data-rich cyber-physical environment from IT/OT convergence suggests a st
 rong need for greater data-driven modelling paradigms and analytics. In th
 is talk\, we provide examples of deep learning in the context of anomaly d
 etection for the cyber-physical protection of transmission protection syst
 ems. We then present a brave new world of opportunities for smart grid cyb
 er-physical security using a data analytics-driven approach.&lt;/p&gt;&lt;br /&gt;&lt;br 
 /&gt;Agenda: &lt;br /&gt;&lt;p&gt;&amp;raquo\; 11:50 &amp;nbsp\;Webinar open&lt;/p&gt;\n&lt;p&gt;&amp;raquo\; 12:
 00&amp;nbsp\; IEEE Southern Alberta announcements&lt;/p&gt;\n&lt;p&gt;&amp;raquo\; 12:05 &amp;nbsp
 \;Presentation by Dr. Kundur&lt;/p&gt;\n&lt;p&gt;&amp;raquo\; 12:45 &amp;nbsp\;Q&amp;amp\;A&lt;/p&gt;\n&lt;
 p&gt;&amp;raquo\; 13:00 &amp;nbsp\;Webinar close&lt;/p&gt;
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