Email Spam Detection

Abstract

The exponential increase in spam emails poses a significant challenge to digital communication and cybersecurity. Traditional machine learning techniques such as Naïve Bayes and Support Vector Machines (SVM) have been widely employed for spam detection but often fall short in handling the evolving nature and complexity of spam content. This study presents an advanced email spam detection model based on Long Short-Term Memory (LSTM) networks, which are known for their strength in processing sequential data and capturing contextual dependencies in textual information. The research adopts a quantitative experimental approach, utilizing a Kaggle dataset comprising 5,572 email samples. Comprehensive preprocessing, including stop-word removal, tokenization, and word embeddings, was employed to enhance data quality. The LSTM model was trained and evaluated using accuracy, precision, recall, and F1-score metrics and compared with SVM and Naïve Bayes classifiers. Experimental results revealed that the proposed LSTM model significantly outperformed traditional models, achieving 98.74?curacy, 97.50% precision, and 98.11?-score. These findings highlight the model’s ability to reduce false positives and adapt to dynamic spam patterns. This research demonstrates that LSTM networks provide a scalable and effective solution for real-time spam detection, with potential applications in email filtering, enterprise communication security, and automated message categorization systems.

Keywords

Introduction

The proposed model’s enhanced performance is attributed to robust preprocessing, hyperparameter tuning, and a specialized focus on email-only spam.

DISCUSSION

Why LSTM is More Effective

• Captures long-term dependencies in email text
• Learns contextual relationships and spam patterns
• Uses dropout layers to prevent overfitting
• Applies the Adam optimizer for efficient convergence

Implications

• Fewer false positives ensure minimal disruption to users.
• The LSTM model is adaptable to new spam patterns.
• Can be scaled and deployed in real-time filtering systems.

4. Conclusion and Future Work

This study has demonstrated the effectiveness of Long Short-Term Memory (LSTM) networks in detecting spam emails with significantly greater accuracy than traditional machine learning techniques such as Naïve Bayes and Support Vector Machines (SVM). Through the use of advanced preprocessing techniques and optimized model architecture, the proposed LSTM model achieved 98.74% accuracy, 97.50% precision, and an F1-score of 98.11%. The experimental results confirm that LSTM networks are particularly suitable for handling sequential and contextual data, which are critical in distinguishing between legitimate and spam emails. Additionally, the model’s robustness in reducing false positives and false negatives indicates its practical applicability in real-world scenarios such as enterprise email systems, webmail platforms, and messaging applications. Despite its high accuracy, the model is not without limitations. The dataset used is relatively small and text-centric, which does not reflect the complexity of multi-modal spam (e.g., spam containing images, attachments, or embedded URLs). Furthermore, the computational demands of LSTM architectures may restrict deployment in resource-constrained environments.

Future Work Recommendations

To enhance the model’s effectiveness and scalability, the following directions are recommended:

• Expand the dataset to include larger and more diverse email sources.
• Explore lightweight LSTM variants (e.g., Distilled LSTM, Edge-LSTM) for mobile deployment.
• Integrate hybrid models combining CNN, Attention Mechanisms, or Transformer-based architectures.
• Develop multi-lingual and multi-modal spam detection systems to increase generalizability.
• Focus on adversarial robustness to resist evolving spam attack strategies.

ACKNOWLEDGEMENTS

• The Dheeraj Iti (author) would like to express sincere gratitude to the faculty and research coordinators of Department of Computer Applications, JSPM University, for their valuable support and guidance throughout this study. Special thanks are extended to the mentors and reviewers who provided constructive feedback during the development and evaluation phases of this research.
• Appreciation is also due to the creators and maintainers of the public datasets and open-source libraries that were instrumental in the implementation of the LSTM-based spam detection system. Lastly, the authors acknowledge the contribution of peers and collaborators who offered technical assistance and moral support during the completion of this work.

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