An Explainable AI-Driven Zero-Day Attack Detection Framework for Securing Edge Devices in Smart Cities

Santhiyakumari N. , Sabarinathan S. , Veerakumar S. , Chandraman M. , Kiruthika G.

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.

Doi: https://doi.org/10.54216/JCHCI.100201

Vol. 10 Issue. 2 PP. 01-11, (2025)