This study presents a metric selection framework and a normalization method for the quantitative assessment of cyber resilience, with a specific focus on availability as a core dimension. To develop a generalizable evaluation model, service types from 1124 organizations were categorized, and candidate metrics applicable across diverse operational environments were identified. Ten quantitative metrics were derived based on five core selection criteria—objectivity, reproducibility, scalability, practicality, and relevance to resilience—while adhering to the principles of mutual exclusivity and collective exhaustiveness. To validate the framework, two availability-oriented metrics—Transactions per Second (TPS) and Connections per Second (CPS)—were empirically evaluated in a simulated denial-of-service environment using a TCP SYN flood attack scenario. The experiment included three phases: normal operation, attack, and recovery. An Area Under the Curve (AUC)-based Normalized Resilience Index (NRI) was introduced to quantify performance degradation and recovery, using each organization’s Recovery Time Objective (RTO) as a reference baseline. This approach facilitates objective, interpretable comparisons of resilience performance across systems with varying service conditions. The findings demonstrate the practical applicability of the proposed metrics and normalization technique for evaluating cyber resilience and underscore their potential in informing resilience policy development, operational benchmarking, and technical decision-making.

Industrial control systems (ICSs) are increasingly interconnected with enterprise IT networks and remote services, which expands the attack surface of operational technology (OT) environments. However, collecting sufficient attack traffic from real OT/ICS networks is difficult, and the resulting scarcity and class imbalance of malicious data hinder the development of intrusion detection systems (IDSs). At the same time, large language models (LLMs) have shown promise for security analytics when system events are expressed in natural language. This study investigates an LLM-based network IDS for a smart-factory OT/ICS environment and proposes a synthetic data generation method that injects domain knowledge into attack samples. Using the ICSSIM simulator, we construct a bottle-filling smart factory, implement six MITRE ATT&CK for ICS-based attack scenarios, capture Modbus/TCP traffic, and convert each request–response pair into a natural-language description of network behavior. We then generate synthetic attack descriptions with GPT by combining (1) statistical properties of normal traffic, (2) MITRE ATT&CK for ICS tactics and techniques, and (3) expert knowledge obtained from executing the attacks in ICSSIM. The Llama 3.1 8B Instruct model is fine-tuned with QLoRA on a seven-class classification task (Benign vs. six attack types) and evaluated on a test set composed exclusively of real ICSSIM traffic. Experimental results show that synthetic data generated only from statistical information, or from statistics plus MITRE descriptions, yield limited performance, whereas incorporating environment-specific expert knowledge is associated with substantially higher performance on our ICSSIM-based expanded test set (100% accuracy in binary detection and 96.49% accuracy with a macro F1-score of 0.958 in attack-type classification). Overall, these findings suggest that domain-knowledge-infused synthetic data and natural-language traffic representations can support LLM-based IDSs in OT/ICS smart-factory settings; however, further validation on larger and more diverse datasets is needed to confirm generality.

Industrial control systems (ICSs) are increasingly interconnected with enterprise IT networks and remote services, which expands the attack surface of operational technology (OT) environments. However, collecting sufficient attack traffic from real OT/ICS networks is difficult, and the resulting scarcity and class imbalance of malicious data hinder the development of intrusion detection systems (IDSs). At the same time, large language models (LLMs) have shown promise for security analytics when system events are expressed in natural language. This study investigates an LLM-based network IDS for a smart-factory OT/ICS environment and proposes a synthetic data generation method that injects domain knowledge into attack samples. Using the ICSSIM simulator, we construct a bottle-filling smart factory, implement six MITRE ATT&CK for ICS based attack scenarios, capture Modbus/TCP traffic, and convert each request–response pair into a natural-language description of network behavior. We then generate synthetic attack descriptions with GPT by combining (1) statistical properties of normal traffic, (2) MITRE ATT&CK for ICS tactics and techniques, and (3) expert knowledge obtained from executing the attacks in ICSSIM. The Llama 3.1 8B Instruct model is fine-tuned with QLoRA on a seven-class classification task (Benign vs. six attack types) and evaluated on a test set composed exclusively of real ICSSIM traffic. Experimental results show that synthetic data generated only from statistical information or from statistics plus MITRE descriptions yield limited performance, whereas incorporating environment-specific expert knowledge enables the model to achieve 99.61% accuracy in binary detection and 95.63% accuracy with a macro F1-score of 0.952 in attack-type classification. These results demonstrate that domain-knowledge–infused synthetic data and natural-language traffic representations can make LLM-based IDSs a practical option for deployment in OT/ICS smart-factory environments.