Improving spam detection techniques using machine learning algorithms

Abstract

Spam detection has become a critical challenge in maintaining the integrity and security of digital communication systems. This study focuses on enhancing spam detection precision by applying the XGBoost algorithm, compared to other machine learning models such as Support Vector Machines (SVM) and Random Forests. Traditional rule-based methods, while straightforward, need to address the increasing sophistication of spam tactics, leading to high rates of false positives and negatives. Machine learning, with its data-driven adaptability, provides a powerful alternative for improving spam detection accuracy. This research evaluates the effectiveness of XGBoost in handling large and complex datasets, leveraging its ability to build robust predictive models by combining weak learners. Performance metrics, including accuracy, precision, recall, and F1-score, are used to assess its efficacy. Additionally, a comparative analysis highlights XGBoost's advantages over SVM, known for its precision in high-dimensional spaces, and Random Forest, valued for its resilience against overfitting. The study emphasizes the importance of parameter optimization and feature engineering in maximizing the performance of spam detection models. This research contributes to the development of more reliable and scalable spam detection frameworks by addressing the diverse attributes of spam messages. The findings offer valuable insights for researchers and practitioners in cybersecurity, underscoring the transformative potential of XGBoost in combating the evolving threat of spam.