LinguTimeX: Explainable AI of Natural Language Detection in Leakage Information with Covert Timing Channels

Abstract CCovert timing channels (CTC) enable surreptitious information leakage through manipulated data transmission timing, posing a severe cybersecurity risk. This research introduces a framework, called LinguTimeX, which utilizes explainable artificial intelligence approach that synergizes linguistic analysis with detection of timing irregularities to pinpoint CTC activities with high accuracy. The LinguTimeX extracts multidimensional features integrating statistical properties of textual content and temporal characteristics. An ensemble of machine learning and deep learning models, tailored for cross-lingual covert channel identification , demonstrates outstanding detection performance, with over 98% precision and recall in Arabic and 81% F1-score in Chinese datasets. Quantitative analysis and visualizations elucidate how innate language complexities govern covert signal amplitudes, guiding targeted optimization of feature engineering. The