Hybrid Spam Filtering Using SVM for Classification and Feature Engineering

Spam emails pose a persistent and escalating challenge in digital communication, resulting in wasted bandwidth, reduced productivity, and increased cybersecurity threats. Traditional rule-based filtering systems have proven inefficient, particularly against evolving spam tactics such as obfuscation and disguised content. This study investigates the application of Support Vector Machine (SVM), a robust supervised machine learning algorithm, for the classification and feature extraction of spam in email datasets. We explore how SVM leverages high-dimensional feature spaces to separate spam from legitimate (ham) emails with precision. The methodology involves preprocessing email data, extracting key linguistic and structural features, and training an SVM classifier on labeled datasets to distinguish spam from ham. Compared to rule-based and other conventional techniques, the SVM approach requires minimal manual intervention and adapts more efficiently to new spam trends. Empirical results demonstrate high accuracy, precision, and recall rates in spam detection, validating the effectiveness of the model. Furthermore, the study identifies the most relevant features contributing to spam classification, offering insights into how modern spammers manipulate email content. These findings not only enhance spam detection capabilities but also contribute to the development of intelligent and adaptive email filtering systems. The study concludes that SVM remains a valuable tool in the fight against spam, especially when integrated with continuous learning and real-time data streams