000			01770nam a22001937a 4500
003			FT8939
005			20260112110733.0
008			260112b ||||| |||| 00| 0 eng d
050			_aQA76.87 M37 2025
100	1		_a Marcos, Patrick L.; Pacatang, Dana Justine D.
245			_aMalicious email detection using enhanced SVM and CBAM-efficientnet-BO
264		1	_cc2025
300			_bUndergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2 unmediated _a unmediated _b unmediated
338			_2 volume _a volume _b volume
505			_aABSTRACT: 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			_2lcc _cMS
999			_c37417 _d37417