BibTeX for a paper by David Kotz at Dartmouth College.
For more information about this paper, visit this web page:
https://kotz.cs.dartmouth.edu/research/arguello-nicprint/index.html

@InProceedings{arguello:nicprint,
  author =        {Cesar Arguello and Beatrice Perez and Timothy J. Pierson and David Kotz},
  title =         {{NICPrint: Wi-Fi NIC Identification via Malformed Preambles}},
  booktitle =     {{Proceedings of the Network and Distributed System Security Symposium (NDSS)}},
  year =          2027,
  month =         {March},
  publisher =     {The Internet Society},
  copyright =     {the authors},
  DOI =           {10.14722/ndss.2027.230760},
  URL =           {https://kotz.cs.dartmouth.edu/research/arguello-nicprint/index.html},
  note =          {Accepted for publication},
  abstract =      {Wi-Fi vulnerabilities are often tied to specific chipsets, yet identifying the chipset behind an observed MAC address from over-the-air transmission alone is an open problem.  We present NICPrint, an identification system that recovers the manufacturer and chipset model of a Wi-Fi network interface (NIC) by remotely probing its digital reception logic. The IEEE 802.11 standard specifies what the Wi-Fi preamble must accomplish but not how, leaving vendors free to make implementation choices in their PHY baseband designs. Without requiring network credentials, I/Q access, CSI, or target cooperation beyond ACK transmission, NICPrint elicits these implementation differences by transmitting frames with controlled preamble corruption and observing the polite-ACK responses to construct a fingerprint. We evaluate NICPrint on 25 devices spanning five major Wi-Fi vendors and four Wi-Fi generations. Same- session cross-validation provides an upper bound of 99\% accuracy for both manufacturer and chipset identification. On previously unseen devices with chipsets represented in training, NICPrint averages 95\% manufacturer and 96\% chipset accuracy across the tested classifiers. Under through-wall, non-line-of-sight, and varying distance conditions measured more than one month after training, mean classifier accuracy is 83\% for manufacturer and 80\% for chipset identification; channel augmentation increases these to 98\% and 93\%, respectively. These results establish NICPrint as a practical Wi-Fi reconnaissance tool.},
}