LEADER 02821nam  2200337   450 
001 9910571792603321
005 20230821112617.0
035   $a(CKB)5580000000325067
035   $a(NjHacI)995580000000325067
035   $a(EXLCZ)995580000000325067
100   $a20230821d2022      uy             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aProceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning /$fMurtuza Jadliwala
210  1$aNew York :$cAssociation for Computing Machinery,$d2022.
215   $a1 online resource (87 pages)
311   $a1-4503-9277-6 
330   $aWe are very pleased to welcome you to the 4th ACM Workshop on Wireless Security and Machine Learning (WiseML). ACM WiseML continues to be the premier venue that brings together members of the machine learning, privacy, security, wireless communications, and networking communities from around the world, and offers them the opportunity to share their latest research findings in these emerging and critical areas. It also offers a shared platform to exchange ideas and foster research collaborations, in order to further advance the state of the art. This year, WiseML will be an in-person event in San Antonio, Texas, USA, and is hosted by the Department of Computer Science and the National Security Collaboration Center at the University of Texas at San Antonio (UTSA). The program will be presented in a single track. The technical program this year features 14 outstanding papers that cover a wide variety of security, privacy, and adversarial machine learning problems relating to wireless networks, communications, mobile networks, 5G/IoT systems, cloud systems, cyber physical systems, smartphones, cognitive radios, and emerging applications. Our call for papers attracted 23 qualified submissions from across the globe. These submissions have been carefully reviewed by six Technical Program Committee (TPC) co-chairs, as well as external experts from academia, industrial research labs, and government organizations. WiseML's exciting technical program is enriched by the keynote talk "Accelerating RF Autonomy for Uncertain Environments," by Dr. John Davies from DARPA, a distinguished leader in the field of machine learning, adaptive radio frequency systems, real-time signal processing, and configurable computing. Warm thanks to the keynote speaker for joining us.
606   $aMachine learning
615  0$aMachine learning.
676   $a006.31
700   $aJadliwala$b Murtuza$0890908
801  0$bNjHacI
801  1$bNjHacl
906   $aBOOK
912   $a9910571792603321
996   $aProceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning$93427525
997   $aUNINA