Vasileios Gkioulos

Vyron Kampourakis, Efstratios Chatzoglou, Vasileios Gkioulos et al.

Wi-Fi is the dominant wireless access technology, but its widespread use also exposes systems to threats such as rogue access points, deauthentication attacks, and other IEEE 802.11-specific vulnerabilities. Although Cyber Ranges (CRs) have become valuable platforms for cybersecurity training and experimentation, existing wireless-oriented solutions mainly target heterogeneous IoT or mobile-network settings, with Wi-Fi typically treated as one among many. As a result, dedicated CR environments for Wi-Fi-specific security experimentation remain limited. This gap is particularly relevant because wireless attacks often require protocol-aware experimentation that is difficult to reproduce in conventional training environments. This paper introduces a conceptual architecture for a Wi-Fi-focused CR tailored to IEEE 802.11 security scenarios and an open-source prototype. The proposed design is grounded in established CR design principles and organized around core infrastructure, learning management and support, monitoring, management, and access-control zones. Structuring the platform into these distinct zones, the architecture supports modularity, scalability, and future extensibility. Part of the design is realized in a prototype publicly available in a GitHub repository that implements the scenario generation, storage, retrieval, and instantiation workflow, offering an initial practical foundation for the proposed architecture. Overall, the paper provides a structured foundation for the future implementation of Wi-Fi-specialized CR platforms for targeted experimentation.

Konstantinos E. Kampourakis, Vasileios Gkioulos, Sokratis Katsikas

Cyber attacks targeting Industrial Control Systems (ICS) have become increasingly sophisticated and hard to identify. Detecting such attacks requires integrating low-level behavioral cues with high-level semantic interpretation, a capability that traditional anomaly detectors lack. This paper presents a Digital Twin (DT)-driven hybrid detection approach that combines deterministic heuristics with systematic, constrained Large Language Model (LLM) reasoning to achieve real-time incident detection. The DT maintains a synchronized, feature-enriched representation of the Secure Water Treatment (SWaT) process, deriving behavioral descriptors. Heuristics identify characteristic signatures of spoofing, valve forcing, denial-of-service, and bias drift, while the LLM is invoked only when heuristics abstain. A constrained JSON schema and semantic plausibility filters ensure physically consistent LLM outputs, and a temporal smoothing layer stabilizes the final decision signal. Evaluation on four canonical SWaT attack scenarios shows that the proposed detector precisely localizes each attack interval with low time-to-detect and zero False Positives (FPs) in the evaluated benign region. Results are consistent across both a local LLaMA model and a cloud-based GPT model, demonstrating the robustness of the constrained hybrid architecture. The findings highlight the potential of DT-guided LLM reasoning as a reliable and interpretable approach to ICS anomaly detection.