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Machine learning

Machine Learning Framework Enhances Email Threat Detection

Strategic Insights

— 1 min read

A new machine learning-driven framework has been introduced, integrating Natural Language Processing (NLP) with a Support Vector Classifier (SVC) to improve email threat detection. The proposed model utilizes feature extraction techniques to capture linguistic patterns indicative of malicious emails. The framework was trained and evaluated on a benchmark dataset, achieving an accuracy of 98.65% and demonstrating superior performance over conventional spam detection systems.

Key Takeaways:

  • The new machine learning framework integrates NLP and SVC to enhance email threat detection, addressing the evolving nature of email-based cyber threats.
  • The proposed model utilizes feature extraction techniques, including one-hot encoding, TF-IDF vectorization, and BERT embeddings, to capture linguistic patterns indicative of malicious emails.
  • The framework was trained and evaluated on a benchmark dataset, achieving an accuracy of 98.65% and demonstrating superior performance over conventional spam detection systems.
  • The results show a significant reduction in false positives and false negatives, improving email security and reliability.
  • The research concluded that the proposed model is an optimized, scalable solution that can adapt to emerging cyber threats and enhance automated email filtering mechanisms.
  • The study aims to contribute to the field of email threat detection by presenting a solution that can improve email security and reduce the reliance on traditional security measures.
  • King Saud University's Researchers Supporting Project provided financial support for this research.
  • The study's findings were published in the Alexandria Engineering Journal, a peer-reviewed scientific journal.

Statistics:

  • 98.65%: The accuracy achieved by the proposed model in detecting malicious emails.
  • 12372: The postal code of King Saud University's Ctr Excellence Informat Assurance CoEIA.
  • 2025: The year in which the research was conducted.
  • 128: The volume number of the Alexandria Engineering Journal.
  • 153-165: The page numbers where the research was published.

Sources:

  • NewsRx LLC. Machine Learning Algorithm for Detecting Suspicious Email Messages Using Natural Language Processing Nlp. Alexandria Engineering Journal, 2025;128:153-165.
  • Elsevier. Alexandria Engineering Journal. Retrieved from www.elsevier.com
  • Kashif Saleem. King Saud University. Retrieved from Ctr Excellence Informat Assurance CoEIA, Riyadh 12372, Saudi Arabia.

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