SPAMGUARD: AN INTEGRATED KALMAN FILTER AND CNN APPROACH FOR EMAIL SPAM CLASSIFICATION
DOI:
https://doi.org/10.29121/ijetmr.v10.i6.2023.1600Keywords:
Kalman, Cnn, Email, Classification, SpamAbstract
Email remains a primary mode of communication for both professional and personal use due to its low cost, accessibility, and widespread adoption. However, the open nature of email systems exposes users to spam — unsolicited, irrelevant, or malicious messages — posing risks such as phishing, fraud, and information overload. Existing spam detection mechanisms face challenges in keeping pace with the evolving strategies used by spammers and must balance aggressive filtering with the risk of legitimate message loss. To address these limitations, this study proposes a novel spam detection framework combining Kalman Filters and Convolutional Neural Networks (CNNs). Kalman Filters are utilized to pre-process and denoise input text data, effectively mitigating irregularities and improving feature consistency. CNNs are then employed to automatically learn hierarchical text representations, enabling robust classification of emails into spam or legitimate categories. The integration of Kalman-based preprocessing with deep learning enhances both detection accuracy and system reliability. Additionally, the system provides a quick summary view of classified emails to assist users in rapidly assessing message content. Experimental results demonstrate the potential of the proposed method to outperform traditional spam detection techniques, offering a scalable and adaptive solution to modern email security challenges.
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