BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Lisbon BEGIN:DAYLIGHT DTSTART:20240331T020000 TZOFFSETFROM:+0000 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:WEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20241027T010000 TZOFFSETFROM:+0100 TZOFFSETTO:+0000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:WET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20241202T171404Z UID:F896CF51-031A-414D-92E7-72B43EB3F082 DTSTART;TZID=Europe/Lisbon:20241023T140000 DTEND;TZID=Europe/Lisbon:20241023T153000 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 . However\, this revolution will not be viable if we cannot provide a secu re connection. The current communication networks are protected by convent ional cryptography\, which is based on complicated mathematical algorithms and/or protocols. However\, the IoT consists of many low-cost devices wit h limited computational capacity and battery power\, which cannot afford c ostly cryptography.\n\nPhysical layer security (PLS) has demonstrated grea t potential in protecting IoT\, because it can achieve security in a light weight manner. This talk will give a comprehensive introduction to our rec ent research on PLS for IoT. In the first part\, we will present an emergi ng device authentication technique based on radio frequency fingerprint id entification (RFFI). There are minute\, unique\, and stable hardware impai rments originating from the manufacturing process\, which can be extracted as device fingerprints to authenticate the identity of IoT devices. We wi ll elaborate on how deep learning is leveraged to enhance RFFI performance . In the second part\, we will introduce key generation from wireless chan nels. The channel characteristics are unpredictable and dynamic\, and thei r randomness can be exploited as the cryptographic keys to enable secure c ommunications. Our research findings on experimental evaluation with pract ical wireless standards including WiFi and LoRa will be presented.\n\nSpea ker(s): Dr. Junqing Zhang\n\nRoom: B5.1\, Dep. Engenharia Eletrotecnica e de Computadores\, Universidade de Coimbra\, Pólo II\, Coimbra\, Centro\, Portugal\, 3030-290\, Virtual: https://events.vtools.ieee.org/m/440638 LOCATION:Room: B5.1\, Dep. Engenharia Eletrotecnica e de Computadores\, Uni versidade de Coimbra\, Pólo II\, Coimbra\, Centro\, Portugal\, 3030-290\, Virtual: https://events.vtools.ieee.org/m/440638 ORGANIZER:ch.vt@ieee-pt.org SEQUENCE:36 SUMMARY:Webinar 'Wireless Security for the Internet of Things: Device Authe ntication and Key Generation' by Dr. Junqing Zhang URL;VALUE=URI:https://events.vtools.ieee.org/m/440638 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. However\, this revolution will not be viable if we cannot provide a secure connection. T he current communication networks are protected by conventional cryptograp hy\, which is based on complicated mathematical algorithms and/or protocol s. However\, the IoT consists of many low-cost devices with limited comput ational capacity and battery power\, which cannot afford costly cryptograp hy.&nbsp\;</span></p>\n<p class="MsoNormal"><span lang="EN-GB">Physical la yer security (PLS) has demonstrated great potential in protecting IoT\, be cause it can achieve security in a lightweight manner. This talk will give a comprehensive introduction to our recent research on PLS for IoT. In th e first part\, we will present an emerging device authentication technique based on radio frequency fingerprint identification (RFFI). There are min ute\, unique\, and stable hardware impairments originating from the manufa cturing process\, which can be extracted as device fingerprints to authent icate the identity of IoT devices. We will elaborate on how deep learning is leveraged to enhance RFFI performance. In the second part\, we will int roduce key generation from wireless channels. The channel characteristics are unpredictable and dynamic\, and their randomness can be exploited as t he cryptographic keys to enable secure communications. Our research findin gs on experimental evaluation with practical wireless standards including WiFi and LoRa will be presented. </span></p> END:VEVENT END:VCALENDAR