aspg-25-4212-1791493003 20261008205643 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cognitive Human-Computer Interaction JCHCI 2771-1471 2771-1463 2025 10 2 Santhiyakumari N. Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India Sabarinathan S. Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India Veerakumar S. Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India Chandraman M. Assistant Professor, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India Kiruthika G. PG Scholar, Department of ECE, Knowledge Institute of Technology, Salem, Tamil Nadu, India The rapid proliferation of edge computing in smart cities has enhanced real-time data processing capabilities, but it has also exposed critical vulnerabilities to sophisticated cyber threats such as zero-day attacks. Traditional signature-based intrusion detection systems often fail to identify these previously unknown threats due to their lack of adaptive intelligence and interpretability. This research proposes an Explainable Artificial Intelligence (XAI)-driven zero-day attack detection framework tailored for edge devices deployed in smart city environments. The proposed system combines deep anomaly detection using a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model with SHAP (SHapley Additive exPlanations)-based interpretability to detect and explain anomalous behaviors in real-time network traffic. The model is trained on diverse datasets mimicking heterogeneous edge devices in smart infrastructures, ensuring robustness and scalability. Experimental results demonstrate high detection accuracy, low false-positive rates, and strong resilience against unseen attack patterns. Moreover, the integration of XAI components provides actionable insights to administrators, thereby enhancing trust, transparency, and decision-making in cybersecurity operations. This framework marks a significant step toward proactive and explainable security solutions for safeguarding smart urban ecosystems. 2025 01 07 4212 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCHCI.100201 https://www.americaspg.com/journal/25/article/4212 https://www.americaspg.com/storage/articles/manuscripts/01KWMRCDNHHVDG2PBWB22F3QWF.pdf