@misc{RennerPozzobonMottok, author = {Renner, Sebastian and Pozzobon, Enrico and Mottok, J{\"u}rgen}, title = {3rd Round Ciphers Evaluation on Microcontrollers}, series = {Lightweight Cryptography Workshop (virtual): May 9-11 2022}, journal = {Lightweight Cryptography Workshop (virtual): May 9-11 2022}, publisher = {National Institute of Standards and Technology (NIST)}, address = {Gaithersburg}, language = {en} } @inproceedings{RennerPozzobonMottok, author = {Renner, Sebastian and Pozzobon, Enrico and Mottok, J{\"u}rgen}, title = {The Final Round: Benchmarking NIST LWC Ciphers on Microcontrollers}, series = {Attacks and Defenses for the Internet-of-Things, 5th International Workshop, ADIoT 2022: Copenhagen, Denmark, September 30, 2022, Revised Selected Papers}, booktitle = {Attacks and Defenses for the Internet-of-Things, 5th International Workshop, ADIoT 2022: Copenhagen, Denmark, September 30, 2022, Revised Selected Papers}, editor = {Li, Wenjuan and Furnell, Steven and Meng, Weizhi}, publisher = {Springer Nature}, address = {Cham}, isbn = {978-3-031-21311-3}, doi = {10.1007/978-3-031-21311-3_1}, pages = {1 -- 20}, abstract = {In this work, we present our benchmarking results for the ten finalist ciphers of the Lightweight Cryptography (LWC) project initiated by National Institute of Standards and Technology (NIST). We evaluate the speed and code size of various software implementations on five different platforms featuring four different architectures. Moreover, we benchmark the dynamic memory utilization of the remaining NIST LWC algorithms on one 32-bit ARM controller. We describe our test cases and methodology and provide some information regarding the design and properties of the finalists before showing and discussing our results. Altogether, we evaluated almost 300 implementations of the 3rd round candidates and pick the most appropriate and best (primary) implementation of each cipher for our comparisons. We include a variant of AES-GCM in our benchmarking in order to be able to compare the state-of-the-art to the novel LWC ciphers. Our research gives an overview over the performance of the latest software implementations of the NIST LWC finalists and shows under which circumstances which candidate is performing the best in our individual test cases. Additionally, we make all benchmarking results, the code for our test framework and every tested implementation available to the public to ensure a transparent testing process.}, language = {en} } @inproceedings{RennerPozzobonMottok, author = {Renner, Sebastian and Pozzobon, Enrico and Mottok, J{\"u}rgen}, title = {Evolving a Boolean Masked Adder Using Neuroevolution}, series = {Attacks and Defenses for the Internet-of-Things, 5th International Workshop, ADIoT 2022: Copenhagen, Denmark, September 30, 2022, Revised Selected Papers}, booktitle = {Attacks and Defenses for the Internet-of-Things, 5th International Workshop, ADIoT 2022: Copenhagen, Denmark, September 30, 2022, Revised Selected Papers}, editor = {Li, Wenjuan and Furnell, Steven and Meng, Weizhi}, publisher = {Springer Nature}, address = {Cham}, isbn = {978-3-031-21310-6}, doi = {10.1007/978-3-031-21311-3_2}, pages = {21 -- 40}, abstract = {The modular addition is a popular building block when designing lightweight ciphers. While algorithms mainly based on the addition can reach very high performance, masking their implementations results in a huge penalty. Since efficient protection against side-channel attacks is a requirement in lots of use cases, we focus on optimizing the Boolean masking of the modular addition. Contrary to recent related work, we target evolving a masked full adder instead of parts of a parallel prefix adder. We study how techniques typically found in neural network evolution and genetic algorithms can be adapted in order to help in evolving an efficiently masked adder. We customize a well-known neuroevolution algorithm, develop an optimized masked adder with our new approach and implement the ChaCha20 cipher on an ARM Cortex-M3 controller. We compare the performance of the protected neuroevolved implementation to solutions found by traditional search methods. Moreover, the leakage of our new solution is validated by a t-test conducted with a leakage simulator. We present under which circumstances our masked implementation outperforms related work and prove the feasibility of successfully using neuroevolution when searching for complex Boolean networks.}, language = {en} } @phdthesis{Renner, author = {Renner, Sebastian}, title = {Secure Lightweight Authenticated Encryption for Critical Infrastructures in the Internet of Things}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:91-diss-20230922-1703211-1-0}, pages = {xix, 135}, abstract = {The cryptographic protection of transmitted data on resource-constraint IoT devices presents a difficult challenge. In this thesis, a benchmarking framework for the performance evaluation of lightweight encryption algorithms is introduced. It is analyzed, if and when novel encryption algorithms are more suitable for IoT use cases than the current standard. Besides comparing the sole performance, the cost for hardening the algorithms against side-channel analysis is discussed.}, language = {en} }