Transparency in Detecting Man-in-the-Middle Attacks on SS7 Networks Using SHAP Explainable AI - Dalam bentuk buku karya ilmiah

This study addresses the security vulnerabilities inherent in the SS7 protocol, with a specific focus on mitigating Man-in-the-Middle (MITM) attacks. Despite technological advancements, persistent security issues in Signaling System No. 7 (SS7) underscore the need to enhance user protection and minimize risks. The primary objective is to leverage Explainable Artificial Intelligence (XAI) to make AI decisions in telecommunications more transparent and justifiable. This research is using advanced machine learning algorithms, including Random Forest, Autoencoders, and K-Means Clustering, integrated into SHapley Additive exPlanations (SHAP) to enhance the interpretability of AI models. The limited availability of specific SS7 datasets forces this study to use existing dataset that was used by another study. The research methodology involves data collection and pre-processing, followed by the implementation and optimization of algorithms to effectively detect and analyze vulnerabilities. The integration of XAI aim