Application of LSTM Neural Networks in Spam SMS Recognition: A Complete Practice from Text Preprocessing to Sequence Modeling

Spam SMS recognition technology has evolved from keyword filtering and shallow machine learning to deep learning, and LSTM has become one of the mainstream solutions due to its excellent sequence modeling capabilities. This article delves into open-source LSTM SMS classification projects, analyzing the complete technical architecture and implementation details from text preprocessing to sequence modeling.

Traditional methods rely on rule matching (easily bypassed by deformed words or homophones) and shallow machine learning (such as Naive Bayes and SVM, which struggle to capture contextual semantics and temporal dependencies). Spam senders use tactics like implicit expressions, split sensitive words, and special character interference to counter these methods, reducing the effectiveness of simple feature extraction. Deep learning (especially LSTM) provides a new solution.

LSTM solves the gradient vanishing problem through gating mechanisms (input gate, forget gate, output gate) and can capture long-distance dependencies. Its advantages in SMS classification include: 1. Context understanding (transmitting hidden states to grasp semantic dependencies); 2. Sequence modeling (processing words in order to build sentence comprehension); 3. Support for variable-length inputs (flexibly handling SMS of different lengths).

Preprocessing includes text cleaning (removing special characters, standardizing to lowercase), word segmentation (handling abbreviations, etc.), stopword removal (needs caution), and stemming/lemmatization (standardizing vocabulary). Vectorization uses word embedding technology to map words to low-dimensional vectors; pre-trained vectors (GloVe/Word2Vec) or task-specific embeddings can be used, and strategies like UNK tokens are employed to handle OOV words.

The architecture includes an embedding layer (dimension 50-300), LSTM layer (number of hidden units, number of layers, dropout regularization), and classification layer (fully connected + sigmoid/softmax). Training uses binary cross-entropy loss and Adam optimizer, considering learning rate decay, batch size, and sequence length. To handle class imbalance, sampling and weight adjustment are used.

Evaluation uses precision (false positives), recall (false negatives), F1 score, confusion matrix, and ROC/AUC. Application considerations include inference latency (model compression, etc.), update mechanisms (regular retraining/online learning), privacy protection (federated learning), and adversarial attack protection (robustness training).

LSTM's limitations are difficulty in parallelizing sequential processing and limited long-distance dependencies. Improvement directions include introducing attention mechanisms, Transformer/pre-trained models (BERT), lightweight models (DistilBERT), and CNN/FastText.

The project demonstrates the process of solving problems with deep learning (analysis → preprocessing → modeling → training → evaluation → deployment). Technology selection must serve business goals; understanding the principles and limitations of tools enables wise decisions. Continuous learning and practice can create value in the security field.