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Improving malicious email detection through novel designated deep-learning architectures utilizing entire email.
Trivikram Muralidharan1, Nir Nissim1
1Malware Lab, Cyber Security Research Center, Ben-Gurion University of the Negev, Israel; Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel.
Summary
Cyber criminals exploit email attacks, including malicious attachments. This study introduces an automated deep ensemble learning framework for advanced email threat detection, achieving superior accuracy over existing methods.
Area of Science:
- Computer Science
- Cybersecurity
- Artificial Intelligence
Background:
- Email remains a primary vector for cyber-attacks, with social engineering and malicious emails causing significant organizational damage.
- Existing solutions struggle to effectively detect complex threats like malicious email attachments, necessitating advanced detection mechanisms.
- Current methods often focus on specific email components, limiting their comprehensive analysis capabilities.
Purpose of the Study:
- To present the first fully automated malicious email detection framework using deep ensemble learning.
- To analyze all email segments (body, header, and attachments) for comprehensive threat identification.
- To eliminate the need for human expert intervention in feature engineering for email security.
Main Methods:
- Developed a deep ensemble learning framework that analyzes the entire email, including body, header, and attachments.
- Trained individual deep learning classifiers on specific email portions to independently utilize full email context.
- Evaluated the framework's generalization capabilities against popular email analysis methods.
Main Results:
- The proposed framework achieved an Area Under the Curve (AUC) of 0.993, surpassing state-of-the-art methods.
- Demonstrated superior performance compared to traditional machine learning models that rely on human expert features.
- Achieved a True Positive Rate (TPR) improvement of 5% over existing malicious email detection techniques.
Conclusions:
- The deep ensemble learning framework offers a highly accurate and automated solution for detecting complex email-based threats.
- Analyzing all email segments holistically enhances detection accuracy and generalization compared to partial analysis methods.
- This approach significantly advances the field of cybersecurity by providing a robust defense against sophisticated email attacks.
