On the effects of neural network-based output prediction attacks on the design of symmetric-key ciphers

Proving resistance to conventional attacks, e.g., differential, linear, and integral attacks, is essential for designing a secure symmetric-key cipher. Recent advances in automatic search and deep learning-based methods have made this time-consuming task relatively easy, yet concerns persist over expertise requirements and potential oversights. To overcome these concerns, Kimura et al. proposed neural network-based output prediction (NN) attacks, offering simplicity, generality, and reduced coding mistakes. NN attacks could be helpful for designing secure symmetric-key ciphers, especially the S-box-based block ciphers. Inspired by their work, we first apply NN attacks to Simon, one of the AND-Rotation-XOR-based block ciphers, and identify structures susceptible to NN attacks and the vulnerabilities detected thereby. Next, we take a closer look at the vulnerable structures. The most vulnerable structure has the lowest diffusion property compared to others. This fact implies that NN attacks may detect such a property. We then focus on a biased event of the core function in vulnerable Simon-like ciphers and build effective linear approximations caused by such an event. Finally, we use these linear approximations to reveal that the vulnerable structures are more susceptible to a linear key recovery attack than the original one. We conclude that our analysis can be a solid step toward making NN attacks a helpful tool for designing a secure symmetric-key cipher.