TSNC: A Timestamp-Driven Stochastic Neural Architecture for Deterministic yet Irreversible Password Hashing

Abstract

Traditional cryptographic hash functions and existing neural hashing schemes suffer from a fundamental vulnerability: their deterministic nature renders them susceptible to rainbow table attacks and precomputation threats. To address this, this paper presents the Temporal-Stochastic Neural Cipher (TSNC), a novel framework that bridges dynamic security with deterministic verification. Unlike static architectures, TSNC eliminates the computationally expensive training phase by leveraging account creation timestamps and passwords to generate cryptographically secure seeds. The framework establishes a robust encryption pipeline through three logically cascaded modules: (1) timestamp-driven dynamic dictionary construction that ensures temporal uniqueness for character embeddings; (2) sequential feature extraction utilizing recurrent transformations to capture inter-character dependencies; and (3) a stochastic neural network with input-dependent dynamic topology, where the network depth and width adaptively evolve based on intermediate features. Theoretical analysis confirms the method's computational irreversibility and intractability against reverse engineering. Comprehensive simulations validate TSNC's security properties, demonstrating strict collision resistance (zero collisions in $10^7$ trials), a profound avalanche effect with output feature vectors exhibiting near-zero cosine similarity under minimal input perturbations, and temporal orthogonality. Furthermore, the algorithm incorporates a constant-time execution mechanism, effectively mitigating timing side-channel attacks while maintaining high computational efficiency. To promote reproducibility and further research, the source code of the proposed algorithm is publicly available at https://github.com/yongqianxiao/TSNC.