Abstract
To solve the compliance risks caused by “weak passwords”, “clear text transmission”, and algorithm misuse, and to help commercial passwords achieve automated and high-precision security assessment, this study proposes a “randomness-space-time” dual-axis fusion framework. This framework contains two core innovations: firstly, on the static ciphertext side, a “randomness visualization” strategy is proposed, which concatenates the NIST nine-term global randomness test values with local non-overlapping template matching results into a vector. After dimensionality reduction by an autoencoder, it is stacked with the original hexadecimal word throttling and bit accumulation graph to form a three-channel grayscale image, which is then input into the ResNet50 for fine-grained recognition of the encryption algorithm. Secondly, on the dynamic traffic side, a spatio-temporal parallel fusion network is designed, the session is fragmented into packet sequences, and “Attention-ResNet50” is run in parallel to extract the “spatial” texture features inside the packets. It uses the Transformer encoder to capture the “time” remote dependencies between data packets to achieve accurate identification of encryption protocols. The results showed that in ciphertext traffic detection, the recognition accuracy of ciphertext and traffic protocols reached 98.83% and 98.25%, both 3%–8% ahead of the control model. The single-sample inference delay was <5 ms, and the throughput was >240 sessions/s. This study couples randomness test statistics with deep vision-sequence models to achieve “ciphertext-traffic” dual-modal collaborative assessment, which can effectively provide compliance detection and defense technical guidance for commercial cryptography applications.
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