Improving malicious email detection through novel designated deep-learning architectures utilizing entire email.

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.