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DTSTAMP:20220325T132924Z
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DTSTART;TZID=America/New_York:20220324T200000
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DESCRIPTION:With the latest advances in medical technology\, more patients 
 today have implantable embedded medical devices. And many of these devices
  are “connected”: devices are now connected to each other and to the i
 nternet\, which leads to the use of the term Internet of Medical Things. W
 iFi/Cellular chips allow medical devices talk to each other\, in addition 
 to the traditional roles of sensing and actuating. But there are also new 
 risks\, because this connectivity makes these devices more prone to hackin
 g.\n\nThis talk focuses on how deep learning techniques can be utilized to
  make these devices secure. This talk covers different problems of securit
 y ranging between internal to communication attacks such as authentication
 \, classification\, prediction on implantable medical devices such as insu
 lin pump implants\, deep brain stimulators and cardiac defibrillator. This
  talk will cover efficient techniques\, such as multi-layer perceptron neu
 ral networks\, recurrent neural networks\, etc. to overcome these problems
 . This talk also discusses how these algorithms can be implemented on the 
 node or on the edge to enable real-time decision making.\n\nSpeaker(s): He
 ena Rathore\, \n\nVirtual: https://events.vtools.ieee.org/m/308471
LOCATION:Virtual: https://events.vtools.ieee.org/m/308471
ORGANIZER:dmancl@acm.org
SEQUENCE:1
SUMMARY:Deep Learning Based Security Solutions for the Internet of Medical 
 Things
URL;VALUE=URI:https://events.vtools.ieee.org/m/308471
X-ALT-DESC:Description: <br /><p>With the latest advances in medical techno
 logy\, more patients today have implantable embedded medical devices. And 
 many of these devices are &ldquo\;connected&rdquo\;: devices are now conne
 cted to each other and to the internet\, which leads to the use of the ter
 m Internet of Medical Things. WiFi/Cellular chips allow medical devices ta
 lk to each other\, in addition to the traditional roles of sensing and act
 uating. But there are also new risks\, because this connectivity makes the
 se devices more prone to hacking.</p>\n<p>This talk focuses on how deep le
 arning techniques can be utilized to make these devices secure. This talk 
 covers different problems of security ranging between internal to communic
 ation attacks such as authentication\, classification\, prediction on impl
 antable medical devices such as insulin pump implants\, deep brain stimula
 tors and cardiac defibrillator. This talk will cover efficient techniques\
 , such as multi-layer perceptron neural networks\, recurrent neural networ
 ks\, etc. to overcome these problems. This talk also discusses how these a
 lgorithms can be implemented on the node or on the edge to enable real-tim
 e decision making.</p>
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