Emerging Cryptanalytic Threats to Lightweight IoT Encryption: Traditional, AI-Based, and Quantum Perspectives

Abstract

The rapid proliferation of Internet of Things (IoT) devices has created an urgent demand for cryptographic algorithms that operate under strict constraints on memory, processing power, and energy consumption. Lightweight encryption algorithms address these requirements, yet their reduced computational complexity and intentionally narrow security margins expose them to an evolving landscape of cryptanalytic attacks. This paper presents a systematic investigation of emerging threats to six representative lightweight IoT ciphers, namely AES-128, PRESENT-80, SPECK32/64, SIMON32/64, GIFT-64, and ASCON-128, examined from three complementary threat perspectives: traditional cryptanalysis, artificial intelligence (AI)-based cryptanalysis, and quantum computing. For traditional threats, we examine differential, linear, algebraic, integral, and slide attack frameworks and map published best-attack results onto each cipher. For AI-based threats, we implement neural distinguisher experiments following the methodology of Gohr (CRYPTO 2019) and supplement them with logistic regression and random forest baselines, evaluating distinguishability across 50,000 plaintext-ciphertext pairs per cipher. For quantum threats, we apply Grover's algorithm complexity reduction to assess post-quantum key security margins. A comprehensive experimental evaluation is conducted comprising: (1) NIST SP 800-22 statistical test suite over 1,000,000 bits per cipher; (2) Strict Avalanche Criterion and Bit Independence Criterion analysis across 5,000 trials with 128 input bit flips each; (3) software performance benchmarking cross-referenced against published FELICS data on ARM Cortex-M3; and (4) a structured comparative framework scoring each cipher on eight dimensions. Results demonstrate that all five block ciphers achieve near-ideal avalanche effects (mean 0.4997 to 0.5001) and pass 12 of 15 NIST randomness tests. Neural distinguisher accuracy remains within the 0.493 to 0.504 range, confirming statistical indistinguishability from random. ASCON-128 exhibits structural transparency under single-block evaluation, which is a known consequence of its sponge-based AEAD construction rather than a cryptographic weakness. Quantum analysis reveals that 64-bit key ciphers (SPECK32/64, SIMON32/64) offer only 32 bits of post-Grover security, falling short of current NIST guidance. The paper concludes with countermeasures addressing each identified threat class and a design framework for quantum-aware, AI-resistant lightweight cryptographic systems.