Improving Email Spam Detection Using Hybrid Naïve Bayes and Support Vector Machine Models

The rapid evolution of adversarial spam within resource-constrained enterprise networks requires an urgent transition away from static heuristic filters. While deep learning transformers provide high accuracy, their heavy computational footprints require expensive hardware acceleration (GPUs) that remains impractical for local edge server deployment. This study bridges this gap by developing a low-overhead, CPU-bound Bootstrap Aggregated (Bagging) Ensemble architecture that fuses the probabilistic throughput of Multinomial Naïve Bayes (MNB) with the high-dimensional geometric separation of Support Vector Machines (SVM). Preprocessed via a rigorous natural language processing pipeline and a sub-linear TF-IDF feature mapping, evaluated against a composite benchmark corpus (N=10,000, 52% ham / 48% spam) combining Enron, SpamAssassin, and institutional logs. The hybrid engine matches the classification precision of optimized lightweight deep transformers (e.g., DistilBERT) while cutting CPU inference latency from 412.5 ms to an ultra-low 21.1 ms, the hybrid engine was evaluated against a 2024–2026 real-world benchmark corpus. Empirical results show the proposed model achieves a verified classification accuracy of 98.42% and an $F_1$-score of 98.39%—matching deep learning precision boundaries while drastically reducing mean CPU inference latency from 412.5 ms to an ultra-low 21.1 ms. This framework establishes an efficient, resource-resilient defense baseline aligned with the NIST Cybersecurity Framework (CSF) 2.0 standards for securing constrained edge environments.

Machine Learning, Hybrid Ensemble, Bagging Strategy, Naive Bayes, Support Vector Machines (SVM), Spam Detection, Institutional Cybersecurity, TF-IDF.

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