TY - GEN
T1 - Neural cryptanalysis of lightweight block ciphers using residual MLPs
AU - Eleftheriadis, Charis
AU - Andronikidis, Georgios
AU - Lytos, Anastasios
AU - Fountoukidis, Eleutherios
AU - Karypidis, Paris Alexandros
AU - Lagkas, Thomas
AU - Argyriou, Vasileios
AU - Nanos, Ioannis
AU - Sarigiannidis, Panagiotis
PY - 2025
Y1 - 2025
N2 - The security of Internet of Things (IoT) devices is a growing concern, given their widespread deployment in environments with limited computational and energy resources. Lightweight block ciphers, such as SIMON and SPECK, are designed to provide efficient cryptographic operations while minimizing computational overhead. However, evaluating their resilience against emerging attack vectors is vital for maintaining robust protection. This paper introduces a neural cryptanalysis approach for evaluating the security of SIMON and SPECK block ciphers, by leveraging a Residual Multi-Layer Perceptron (ResMLP) model in order to approximate the encryption and decryption processes. Experimental results demonstrate the effectiveness of the approach in revealing vulnerabilities, showcasing its efficiency and scalability in performing neural cryptanalysis on lightweight block ciphers.
AB - The security of Internet of Things (IoT) devices is a growing concern, given their widespread deployment in environments with limited computational and energy resources. Lightweight block ciphers, such as SIMON and SPECK, are designed to provide efficient cryptographic operations while minimizing computational overhead. However, evaluating their resilience against emerging attack vectors is vital for maintaining robust protection. This paper introduces a neural cryptanalysis approach for evaluating the security of SIMON and SPECK block ciphers, by leveraging a Residual Multi-Layer Perceptron (ResMLP) model in order to approximate the encryption and decryption processes. Experimental results demonstrate the effectiveness of the approach in revealing vulnerabilities, showcasing its efficiency and scalability in performing neural cryptanalysis on lightweight block ciphers.
KW - cryptanalysis
KW - deep learning
KW - lightweight block ciphers
KW - SIMON
KW - SPECK
U2 - 10.1109/CSR64739.2025.11130149
DO - 10.1109/CSR64739.2025.11130149
M3 - Conference contribution
AN - SCOPUS:105016103533
SN - 9798331535926
T3 - Proceedings of the 2025 IEEE International Conference on Cyber Security and Resilience, CSR 2025
SP - 936
EP - 943
BT - Proceedings of the 2025 IEEE International Conference on Cyber Security and Resilience, CSR 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Cyber Security and Resilience, CSR 2025
Y2 - 4 August 2025 through 6 August 2025
ER -