Domain Name Identification with Sequence Matrix Classification

The patent transforms the domain name identification problem from character-level comparison to sequence matrix-level analysis. By converting domain names into sequence matrices and using neural network models (LSTM, GRU, CRNN) to extract deep features, the system achieves higher identification accuracy. The parameter transformation from simple character features to complex sequence patterns enables the system to detect malicious domain names more reliably without false positives.

The patent replaces traditional mechanical character comparison methods with neural network-based sequence modeling. The LSTM, GRU, and CRNN models automatically learn and extract features from domain name sequences, eliminating the need for manual feature engineering and character-by-character analysis. This substitution dramatically improves both classification efficiency and precision, as the neural networks can process domain names end-to-end and identify malicious patterns that traditional methods miss.

The patent implements self-service through automatic feature extraction and sequence matrix generation. The system automatically converts domain names into sequence matrices and uses neural networks to extract features without requiring manual creation or maintenance of character feature libraries. The models are trained once and then autonomously classify domain names, eliminating the need for continuous updates to feature libraries and reducing system complexity despite using advanced neural network architectures.