Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Future Proofing IoT Embedded Platforms for Cryptographic Primitives Support

: Plaga, S.; Wiedermann, N.; Niedermaier, M.; Giehl, A.; Newe, T.


Newe, Thomas (Hrsg.) ; Institute of Electrical and Electronics Engineers -IEEE-:
Twelfth International Conference on Sensing Technology, ICST 2018 : Limerick, Ireland, 04 Dec - 06 Dec 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-5147-6
ISBN: 978-1-5386-5146-9
ISBN: 978-1-5386-5148-3
International Conference on Sensing Technology (ICST) <12, 2018, Limerick>
Fraunhofer AISEC ()

Information security is an important property in areas with distributed and decentralized communication like the Internet of Things (IoT) or Wireless Sensor Nodes (WSNs). Secure communication realises the protection goals of confidentiality, integrity, and authenticity, which are implemented by cryptographic functions. These functions need to evolve steadily in order to catch up with new attack vectors employed by cyber-criminals. This cryptographic evolution brings an increase of resource demand and consumption with it as cryptographic functions rise in complexity. The demand is difficult to satisfy by embedded platforms since they are often limited in their resources due to design efficiency. Therefore, adequate resource buffering is a crucial task in designing embedded systems that are future proof from a security point of view. In this work, we introduce a methodology for comparable resource benchmarking of cryptographic functions on embedded systems. Our approach enables designers and developers of embedded systems to achieve comparable results over an extended range of algorithms and implementations. This aids in the estimation of the cryptographic resource footprint. Further, we develop a measurement architecture for experimentation on different embedded platforms. We conduct a sample of reference measurements confirming well-known patterns in cryptography showing the validity of our framework. Finally, we argue for an open collaboration platform for sharing of benchmark results conducted with the framework.