Towards Transparent Cybersecurity: The Role of Explainable AI in Mitigating Spam Threats

Abstract

Cybersecurity threats, particularly spam SMS, are increasingly sophisticated, demanding more advanced detection systems. Traditional spam detection methods fall short due to their ineffectiveness against novel threats and lack of transparency. This paper investigates the role of Explainable Artificial Intelligence (XAI) in spam detection, emphasizing the interpretability of AI-driven systems through SHapley Additive explanations (SHAP). We propose a hybrid model combining BERT with Random Forest (RF) and Artificial Neural Networks (ANN) for spam detection, and employ SHAP values to elucidate the decision-making process. The study demonstrates that our XAI approach not only improves the accuracy of spam detection but also enhances the transparency and trustworthiness of the predictions. These findings suggest that the incorporation of XAI into spam detection models is not only beneficial but necessary for future cybersecurity measures. Our research invites further exploration into other XAI techniques and their applications in real-world scenarios.