Deep Learning Based Side Channel Attacks on Lightweight Cryptography (Student Abstract)


  • Alexander Benjamin Brown University
  • Jack Herzoff Boise State University
  • Liljana Babinkostova Boise State University
  • Edoardo Serra Boise State University



Lightweight GIFT-128, Hamming Weight Model, Side Channel Attacks, Deep Learning Based Attacks


Computing devices continue to be increasingly spread out within our everyday environments. Computers are embedded into everyday devices in order to serve the functionality of electronic components or to enable new services in their own right. Existing Substitution-Permutation Network (SPN) ciphers, such as the Advanced Encryption Standard (AES), are not suitable for devices where memory, power consumption or processing power is limited. Lightweight SPN ciphers, such as GIFT-128 provide a solution for running cryptography on low resource devices. The GIFT-128 cryptographic scheme is a building block for GIFT-COFB (Authenticated Encryption with Associated Data), one of the finalists in the ongoing NIST lightweight cryptography standardization process (NISTIR 8369). Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. In this paper we present experimental results and comparative study of Deep Learning (DL) based Side Channel Attacks on lightweight GIFT-128. To our knowledge, this is the first study of the security of GIFT-128 against DL-based SCA attacks.




How to Cite

Benjamin, A., Herzoff, J., Babinkostova, L., & Serra, E. (2022). Deep Learning Based Side Channel Attacks on Lightweight Cryptography (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12911-12912.