2771-1463ISSN (Online)
2771-1471ISSN (Print)
Volume 10 , Issue 2 , PP: 01-11, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Santhiyakumari N. 1 * , Sabarinathan S. 2 , Veerakumar S. 3 , Chandraman M. 4 , Kiruthika G. 5
Doi: https://doi.org/10.54216/JCHCI.100201
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.
Explainable AI (XAI) , Zero-Day Attack Detection , Edge Computing , Smart Cities , CNN-LSTM , SHAP , Anomaly Detection , Cybersecurity , Intrusion Detection System (IDS) , Interpretable Deep Learning
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