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DTSTAMP:20250303T221646Z
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DESCRIPTION:The Internet of Things (IoT) is a disruptive technology that ha
 s fundamentally transformed our everyday life\, including many exciting ap
 plications such as smart cities\, smart homes\, connected healthcare\, etc
 . This revolution will not be viable if we cannot provide a secure connect
 ion. The current communication networks are protected by conventional cryp
 tography\, which is based on complicated mathematical algorithms and/or pr
 otocols. However\, the IoT consists of many low-cost devices with limited 
 computational capacity and battery power\, which cannot afford costly cryp
 tography.\n\nPhysical layer security (PLS) has demonstrated great potentia
 l in protecting IoT\, because it can achieve security in a lightweight man
 ner. This talk will give a comprehensive presentation to our recent resear
 ch on PLS for IoT. In the first part\, we will present an emerging device 
 authentication technique based on radio frequency fingerprint identificati
 on (RFFI). There are minute\, unique\, and stable hardware impairments ori
 ginating from the manufacturing process\, which can be extracted as device
  fingerprints to authenticate the identity of IoT devices. We will elabora
 te on how deep learning is leveraged to enhance RFFI performance. In the s
 econd part\, we will introduce key generation from wireless channels. The 
 channel characteristics are unpredictable and dynamic\, and their randomne
 ss can be exploited as the cryptographic keys to enable secure communicati
 ons. Our research findings on experimental evaluation with practical wirel
 ess standards including WiFi and LoRa will be presented.\n\nVirtual: https
 ://events.vtools.ieee.org/m/469987
LOCATION:Virtual: https://events.vtools.ieee.org/m/469987
ORGANIZER:jche629@uwo.ca
SEQUENCE:10
SUMMARY:Wireless Security for the Internet of Things: Device Authentication
  and Key Generation
URL;VALUE=URI:https://events.vtools.ieee.org/m/469987
X-ALT-DESC:Description: <br /><p class="MsoNormal"><span lang="EN-GB">The I
 nternet of Things (IoT) is a disruptive technology that has fundamentally 
 transformed our everyday life\, including many exciting applications such 
 as smart cities\, smart homes\, connected healthcare\, etc. This revolutio
 n will not be viable if we cannot provide a secure connection. The current
  communication networks are protected by conventional cryptography\, which
  is based on complicated mathematical algorithms and/or protocols. However
 \, the IoT consists of many low-cost devices with limited computational ca
 pacity and battery power\, which cannot afford costly cryptography. </span
 ></p>\n<p class="MsoNormal"><span lang="EN-GB">&nbsp\;</span></p>\n<p clas
 s="MsoNormal"><span lang="EN-GB">Physical layer security (PLS) has demonst
 rated great potential in protecting IoT\, because it can achieve security 
 in a lightweight manner. This talk will give a comprehensive presentation 
 to our recent research on PLS for IoT. In the first part\, we will present
  an emerging device authentication technique based on radio frequency fing
 erprint identification (RFFI). There are minute\, unique\, and stable hard
 ware impairments originating from the manufacturing process\, which can be
  extracted as device fingerprints to authenticate the identity of IoT devi
 ces. We will elaborate on how deep learning is leveraged to enhance RFFI p
 erformance. In the second part\, we will introduce key generation from wir
 eless channels. The channel characteristics are unpredictable and dynamic\
 , and their randomness can be exploited as the cryptographic keys to enabl
 e secure communications. Our research findings on experimental evaluation 
 with practical wireless standards including WiFi and LoRa will be presente
 d.</span></p>
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