@article{SiddiqiDoerrStrydis2020,
  author    = {Siddiqi, Muhammad Ali and D{\"o}rr, Christian and Strydis, Christos},
  title     = {IMDfence},
  series = {IEEE access},
  volume    = {8},
  journal   = {IEEE access},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {Piscataway},
  issn      = {2169-3536},
  doi       = {10.1109/ACCESS.2020.3015686},
  pages     = {147948 -- 147964},
  year      = {2020},
  abstract  = {Over the past decade, focus on the security and privacy aspects of implantable medical devices (IMDs) has intensified, driven by the multitude of cybersecurity vulnerabilities found in various existing devices. However, due to their strict computational, energy and physical constraints, conventional security protocols are not directly applicable to IMDs. Custom-tailored schemes have been proposed instead which, however, fail to cover the full spectrum of security features that modern IMDs and their ecosystems so critically require. In this paper we propose IMDfence, a security protocol for IMD ecosystems that provides a comprehensive yet practical security portfolio, which includes availability, non-repudiation, access control, entity authentication, remote monitoring and system scalability. The protocol also allows emergency access that results in the graceful degradation of offered services without compromising security and patient safety. The performance of the security protocol as well as its feasibility and impact on modern IMDs are extensively analyzed and evaluated. We find that IMDfence achieves the above security requirements at a mere less than 7\% increase in total IMD energy consumption, and less than 14 ms and 9 kB increase in system delay and memory footprint, respectively.},
  language  = {en}
}
@misc{KliemeTietzMeinel2018,
  author    = {Klieme, Eric and Tietz, Christian and Meinel, Christoph},
  title     = {Beware of SMOMBIES},
  series = {The 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018)/the 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018)},
  journal   = {The 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018)/the 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-4387-7},
  issn      = {2324-9013},
  doi       = {10.1109/TrustCom/BigDataSE.2018.00096},
  pages     = {651 -- 660},
  year      = {2018},
  abstract  = {Several research evaluated the user's style of walking for the verification of a claimed identity and showed high authentication accuracies in many settings. In this paper we present a system that successfully verifies a user's identity based on many real world smartphone placements and yet not regarded interactions while walking. Our contribution is the distinction of all considered activities into three distinct subsets and a specific one-class Support Vector Machine per subset. Using sensor data of 30 participants collected in a semi-supervised study approach, we prove that unsupervised verification is possible with very low false-acceptance and false-rejection rates. We furthermore show that these subsets can be distinguished with a high accuracy and demonstrate that this system can be deployed on off-the-shelf smartphones.},
  language  = {en}
}