A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers

TL;DR

This paper presents MIND-Crypt, an ML-based framework to evaluate the indistinguishability of lightweight block ciphers like SPECK and SIMON, finding that these ciphers resist ML-based cryptanalysis, confirming their security for IoT applications.

Contribution

Introduction of MIND-Crypt, a novel ML framework for assessing cryptographic indistinguishability of lightweight block ciphers under known plaintext attacks.

Findings

ML models achieve near-random accuracy on ciphertexts

Ciphertexts do not reveal exploitable cryptographic patterns

Existing lightweight ciphers are secure against ML-based attacks

Abstract

Indistinguishability is a fundamental principle of cryptographic security, crucial for securing data transmitted between Internet of Things (IoT) devices. This principle ensures that an attacker cannot distinguish between the encrypted data, also known as ciphertext, and random data or the ciphertexts of the two messages encrypted with the same key. This research investigates the ability of machine learning (ML) in assessing indistinguishability property in encryption systems, with a focus on lightweight ciphers. As our first case study, we consider the SPECK32/64 and SIMON32/64 lightweight block ciphers, designed for IoT devices operating under significant energy constraints. In this research, we introduce MIND-Crypt, a novel ML-based framework designed to assess the cryptographic indistinguishability of lightweight block ciphers, specifically the SPECK32/64 and SIMON32/64 encryption algorithm in CBC mode (Cipher Block Chaining), under Known Plaintext Attacks (KPA). Our approach involves training ML models using ciphertexts from two plaintext messages encrypted with same key to determine whether ML algorithms can identify meaningful cryptographic patterns or leakage. Our experiments show that modern ML techniques consistently achieve accuracy equivalent to random guessing, indicating that no statistically exploitable patterns exists in the ciphertexts generated by considered lightweight block ciphers. Furthermore, we demonstrate that in ML algorithms with all the possible combinations of the ciphertexts for given plaintext messages reflects memorization rather than generalization to unseen ciphertexts. Collectively, these findings suggest that existing block ciphers have secure cryptographic designs against ML-based indistinguishability assessments, reinforcing their security even under round-reduced conditions.