The ISC International Journal of Information Security

Abstract Intrusion Detection Systems (IDS) are vital for defending modern networks against emerging cyber threats, including zero-day attacks. In this article, we introduce GAT-AID (Graph Attention-based Anomaly and Intrusion Detection), an IDS architecture that integrates Graph Attention Networks (GATs), Multi-Layer Perceptron (MLP) classifiers, and Autoencoders. The proposed methodology represents network traffic as a graph, allowing GAT to extract complex node-wise associations across traffic flows. The embeddings generated are further processed through a dual-branch architecture, an MLP-based classifier for identifying known attack types, and an Autoencoder-based anomaly detector for flagging zero-day intrusions. The proposed GAT-AID methodology is evaluated on two widely used benchmark datasets, namely CICIDS2017 and UNSW-NB15. The experiment results demonstrate that it outperforms conventional IDS baselines, including SVM, Random Forest, CNN, and GCN models, achieving higher detection rates, improved robustness against unseen threats, and greater adaptability to evolving network environments. These findings suggest that GAT-AID is an effective and scalable solution for intelligent, real-time intrusion detection.

Abstract Ensuring secure and continuous access in smart environments requires dynamic and context-aware policy enforcement, which traditional access control models cannot adequately support. This paper presents SmartUcon, a model-driven domain-specific modeling language (DSML) based on the UCON+ model, designed for specifying and managing usage control policies in IoT-enabled and cyber-physical systems. SmartUcon provides a Sirius-based graphical editor for intuitive policy modeling, along with an EGL-based code generator that automatically transforms high-level models into executable Axiomatics Language for Authorization (ALFA) policies. The semantics of the DSML are defined translationally via explicit mapping rules to ALFA, ensuring semantic preservation. The framework further incorporates design-time conflict detection and model-time validation using the Epsilon Validation Language (EVL) to reduce policy misconfigurations. To evaluate its applicability, 14 case studies were developed across five domains—smart vehicles, smart homes, smart cities, healthcare, and industrial systems—while a user study with 32 participants and statistical significance tests confirm the usability advantages of graphical modeling. A structural comparison with related metamodels confirms its superior coverage of obligations, advice, and device interactions. Overall, SmartUcon offers a practical and extensible model-driven solution for secure and trustworthy policy enforcement in smart environments.