Sarker, Sumit KantiRahman, Mohammad ShoaibAl Prince, AbdullahAl Mahmud Riaz, AbdulSumon, Md Shakhauat HossanSakib, Md. Tauhidur RahmanSohanoor, SifatTalha, Md Abu2026-07-302026-07-302025-01-01S. K. Sarker et al., "Hybrid Email Filtering Using TF-IDF and BERT Enhanced Spam Detector," 2025 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), Gazipur, Bangladesh, 2025, pp. 1-6, doi: 10.1109/NCIM65934.2025.11160311.97983315554292-s2.0-105017954614https://hdl.handle.net/10361/28706Email spam detection is a crucial cybersecurity challenge requiring accurate and efficient filtering methods. This study introduces the TF-IDF and BERT Enhanced Spam Detector (TBESD), a hybrid deep learning model that integrates statistical and contextual text analysis for robust spam classification. TF-IDF captures essential n-gram patterns, while BERT embeddings extract deep semantic features. These feature vectors are combined and processed through a multilayer perceptron (MLP) with dropout regularization and ReLU activation for optimal classification. The methodology includes text preprocessing (normalization, stopword removal, tokenization), feature extraction (TF-IDF, BERT embeddings), and model training using the Adam optimizer with binary cross-entropy loss. Evaluated on a publicly available dataset, TBESD achieved an impressive accuracy of 98.88%, precision of 98.44%, recall of 99.44%, F1-score of 98.94%, and an AUC score of 0.9990. This study demonstrates the superiority of hybrid models over conventional methods, highlighting the role of advanced NLP techniques in cybersecurity.6 pagesen-USfalseAUC scoreBERTHybrid modelSpam detectionTBESDTF-IDFComputational complexity.Internet--Safety measures.Electronic mail systems.Conference Proceeding10.1109/NCIM65934.2025.11160311