Integrating Artificial Intelligence into Undergraduate Cybersecurity Education: A Course Design for Threat Detection, Explainability, and Ethical Resilience (Journal of The Colloquium for Information Systems Security Education, 2026)
This paper introduces an undergraduate course, Artificial Intelligence Applications in Cybersecurity, designed to equip students with Artificial Intelligence (AI) and Machine Learning (ML) skills to address modern cyber threats. The curriculum integrates supervised and unsupervised learning, deep learning, explainable AI (XAI), adversarial ML, and ethical considerations. Using accessible tools (Python, Google Colab) and real-world datasets (e.g., NSL-KDD, CICIDS2017, malware corpora), students complete phased projects progressing from classical ML baselines to deep learning with interpretability (SHAP/LIME) and robustness against adversarial attacks (FGSM/PGD with mitigation). The course aligns with data science and cybersecurity workforce frameworks, emphasizing reproducibility, communication, and responsible AI practices.
Keywords: Artificial Intelligence (AI); Cybersecurity Education; Machine Learning (ML); Explainable AI (XAI); Adversarial Machine Learning; Ethics and Fairness; Undergraduate Curriculum Design; Workforce Development