Paper 2025/1069

PRESENT Full Round Emulation : Structural Flaws and Predictable Outputs

Abstract

The Internet of Things (IoT) has become integral to modern life, enabling smart cities, healthcare, and industrial automation. However, the increasing connectivity of IoT devices exposes them to various cyber threats, necessitating robust encryption methods. The PRESENT cipher, a lightweight block cipher, is well-suited for resource-constrained IoT environments, offering strong security with minimal computational overhead. This paper explores the application of deep learning (DL) techniques in cryptanalysis, specifically using an Aggregated Perceptron Group (APG) Model, which employs a Multi-Layer Perceptron (MLP) to predict input-output relations for each round of the PRESENT cipher’s encryption, excluding the key. This approach focuses solely on emulating the cipher's Substitution Permutation Network (SPN), capturing its essential structure and revealing the structural flaws in the way data is transformed through rounds. The models are chained together to generate the final ciphertext for any 64-bit plaintext with high accuracy, reducing the probability form a random guess of $2^{64}$. The results demonstrate the potential of DL models in cryptanalysis, providing insights into the security of lightweight ciphers in IoT communications and highlighting the practicality of deep learning for cryptographic analysis on standard computing systems.