This research aims to improve cybersecurity measures by addressing cyber threats and promptly identifying adverse events and anomalies in real-time. A primary focus is the development of a novel Intrusion Detection System (IDS) that overcomes the limitations of existing state-of-the-art systems. The study currently investigates several IDS models grounded in context-based learning, comparing their efficacy. This research applies NLP techniques to augment the precision and contextual comprehension of IDS alerts. This innovative approach bridges the gap between conventional cybersecurity practices and advanced linguistic analysis, fostering the emergence of more context-aware IDS with explainable alerts.