| 000 -LEADER |
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| fixed length control field |
01770nam a22001937a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
FT8939 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260112110733.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260112b ||||| |||| 00| 0 eng d |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.87 M37 2025 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Marcos, Patrick L.; Pacatang, Dana Justine D. |
| 245 ## - TITLE STATEMENT |
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| Title |
Malicious email detection using enhanced SVM and CBAM-efficientnet-BO |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Other physical details |
Undergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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| Source |
text |
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text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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unmediated |
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unmediated |
| Media type code |
unmediated |
| 338 ## - CARRIER TYPE |
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volume |
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volume |
| Carrier type code |
volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: The increasing sophistication of malicious emails poses a significant challenge to traditional classification methods, as text-only models fail to address the diverse nature of evolving threats, including malicious attachments. This study aims to develop a robust multi-modal email classification framework that integrates textual and visual analysis to enhance detection accuracy. The proposed method employs an enhanced Support Vector Machine (SVM) with TF-IDF and Class Variance (TF-IDF-CV) for textual analysis, while malicious attachments are detected using GGE-based visualization with CBAM-EfficientNet-BO. To improve decision-making, an XGBoost ensemble model combines predictions from both modalities , addressing challenges with heterogenous email structures and obfuscated content. Experimental evaluation demonstrates the effectiveness of the proposed framework, achieving 99.6% accuracy. The results indicate that integrating textual and visual analysis significantly improves the detection of complex email threats, offering a practical solution for modern malicious email detection challenges. |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
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| Item type |
Thesis/Dissertation |