# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
## 6 Conclusions  
The proposed model is a comprehensive and functional framework that detects spam emails by integrating Long Short-Term Memory (LSTM) with Gated Recurrent Units (GRUs). It analyzes the growing challenges of spam classification using the Enron-Spam dataset, following thorough textual preprocessing that cleaned HTML, tokens, and removed stop words before vectorizing text with TF-IDF to prepare the input for modeling. The dataset underwent exploratory data analysis and feature engineering stages, resulting in high-quality and representative data, thus providing reliable training conditions. The model structure was designed with recurrent layers to identify short-term and long-term patterns in textual information. Initially, the design included an LSTM layer, but experimental testing showed that combining GRU with LSTM offered the best balance between complexity and performance. Various dropout regularization stages were implemented to prevent overfitting, alongside testing numerous hyperparameters such as dropout rates and sequence lengths, which were optimized by assessing validation accuracy through successive experiments. The model was developed using the Adam optimizer in conjunction with binary cross-entropy loss, generating assessment scores based on accuracy, precision, recall, and F-1 score. The evaluation metrics indicated strong performance results achieved by the model, with minimal overfitting and consistent validation metrics. The study highlights its primary advantage by directly evaluating the model against actual application specifications. The model became scalable for processing extensive email data through enhanced components, recurrent units, and architectural optimization to achieve rapid computation and data delivery. The application functions effectively as an enterprise-level cloud-based email filter by merging swift performance with resource-efficient methodologies to create stable systems. The deep learning system offers superior performance compared to Naive Bayes and k-Nearest Neighbors by employing advanced pattern recognition of evolving spam tactics and complex linguistic analysis to better circumvent spam evasion methods. These environmental shifts in spam campaigns necessitate protective measures as such capabilities remain essential for this specific context. The detection algorithms utilized in deep learning solutions yield efficient results during spam identification tasks. The solution leverages operational requirements to integrate academic research findings from institutions, providing valuable advantages for both theoretical and practical objectives. Scientific research must advance modern detection techniques for monitoring live chat spam patterns using Transformer models combined with attention mechanisms and continuous learning strategies. Conceptualization, S.S. and Z.U.I. ; methodology, S.S., Z.U.I., and S.A.I. ; software, S.S. and S.S.U.H. ; validation, H.A. and M.F.K. ; investigation, S.S.U.H. and H.A. ; resources, M.F.K. ; writing--original draft preparation, S.S. and Z.U.I. ; writing--review and editing, S.S.U.H. and H.A. ; visualization, S.A.I. ; supervision, S.S.U.H. and H.A. ; project administration, M.F.K. ; funding acquisition, M.F.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is partly supported by the National Natural Science Foundation of China under Grant No. W2433158 and in part by the Guangzhou University Postdoctoral startup funding under Grant No. 624021-117. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data we used were derived from the [Enron-Spam datasets] and is available at [([[http://www2.aueb.gr/users/ion/data/enron-spam/](http://www2.aueb.gr/users/ion/data/enron-spam/)]([http://www2.aueb.gr/users/ion/data/enron-spam/](http://www2.aueb.gr/users/ion/data/enron-spam/))) [28], accessed on 23 March 2024]. We captured all six preprocessed, malware-free datasets.