As cyber threats become increasingly sophisticated, ensuring the security of computer networks is more critical than ever. Intrusion Detection Systems (IDS) help identify potential cyberattacks, but many rely on Artificial Intelligence techniques that act as “black boxes,” making their decisions difficult for humans to understand or trust. Security analysts or users do not know what is going on “under the hood” in these models and do not understand the model's reasons for making predictions. This project aims to improve the transparency of these systems by developing a novel Explainable Artificial Intelligence (XAI) technique, and using that to develop Explainable Intrusion Detection Systems (X-IDS). The key idea is to incorporate the time-based patterns inherent in network security data. This will enable security professionals to better understand why an IDS flags certain activities as threats; improving trust, accountability, and decision-making in cybersecurity. By advancing explainable AI for time-sensitive security applications, this project supports national cybersecurity efforts and enhances the reliability of AI-driven defense mechanisms. In addition, we will develop and publish hands-on lab exercises for K-12 students related to the research. Our approach is to develop Temporal Eclectic Rule Extraction (TERE), a novel white-box XAI method for IDS. Unlike existing approaches that rely on black-box surrogate models, TERE will extract human-readable decision rules