Improved integral neural distinguisher model for lightweight cipher PRESENT

Abstract

PRESENT is a lightweight block cipher, which has attracted many scholars to research its security. In 2022, Zahednejad et al. proposed the integral neural distinguisher on round-reduced PRESENT. In this paper, a new integral neural distinguisher for PRESENT is constructed. In contrast to Zahednejad’s works, the proposed integral neural distinguisher can improve the number of attack rounds in one round. This paper proposes a new data format (invP0n,invP1n,…,invP15n,invS0n,invS1n,…,invS15n), which can exposes more features of PRESENT previous round ciphertext. Simultaneously, this paper incorporates MBConv module into the convolutional layers of DenseNet, which enable the neural network to identify a greater variety of features in ciphertext. The data format of the paper is analysed. The results of the analysis show that the data format in this paper is able to identify more features compared to Zahednejad’s data format. Further to this, experiments are performed on PRESENT using the integral neural distinguisher in this paper. The experimental results show that the changes make to the neural network and data format have improved the accuracy of distinguishers. Finally, key recovery attacks are conducted on the integral neural distinguishers of SmallPresent-(8) to demonstrate the efficacy of the distinguisher proposed in this paper. The results demonstrate that the key recovery success rates for 5-round and 6-round are 98% and 90%, considering error bits within a range of two bits.