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

2024-05-30

AI Summary

This work addresses the empirical evaluation of encryption indistinguishability for lightweight block ciphers—SPECK32/64 and SIMON32/64—under known-plaintext attacks (KPA). We propose MIND-Crypt, the first machine learning–driven empirical analysis framework tailored for IoT scenarios. Operating in CBC mode, MIND-Crypt employs supervised models—including CNN, LSTM, and XGBoost—to distinguish ciphertexts using plaintext–ciphertext pairs from multiple reduced-round variants. Experimental results show that all models achieve classification accuracy consistently at 50% ± 0.5% under KPA—statistically indistinguishable from random guessing. Further analysis confirms that high accuracy stems from memorization of training data rather than generalization, thereby empirically validating strong indistinguishability for both ciphers in CBC mode. To our knowledge, this is the first systematic application of machine learning to empirically verify indistinguishability of lightweight ciphers. The framework establishes a novel paradigm for security assessment of cryptographic primitives in resource-constrained environments.

Technology Category

• Machine Learning: Privacy
• Cognitive Modeling & Cognitive Systems: Analogy
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Applications of cryptography
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

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.

Problem

• Assessing cryptographic indistinguishability of lightweight block ciphers using ML
• Evaluating SPECK32/64 and SIMON32/64 ciphers under Known Plaintext Attacks
• Determining if ML can detect patterns in ciphertexts for security validation

Innovation

• ML-based framework for cipher indistinguishability assessment
• Evaluates SPECK32/64 and SIMON32/64 in CBC mode
• Uses Known Plaintext Attacks to test ML pattern detection