The remarkable development of the Internet has made our lives very convenient, such as through the ability to instantaneously transmit individual pictures. As a result, cyber-attacks are also being developed and increasing, and the computer/mobile devices we use can become infected with viruses in an instant. Rapid cyber situational awareness is essential to prepare for such cyber-attacks. Accelerating cyber situational awareness requires Cyber Common Operational Pictures, which integrate and contextualize numerous data streams and data points. Therefore, we propose a Cyber Common Operational Pictures framework and criteria for rapid cyber situation awareness. First, the system reaction speed based on the user’s request and the standard for easily recognizing the object shown on the screen are presented. Second, standards and frameworks for five types of visualization screens that can directly recognize and respond to cyber-attacks are presented. Third, we show how a system was constructed based on the proposed framework, as well as the results of an experiment on the response time of each visualization screen. As a result of the experiment, the response speed of the 5 visualization screens was about 0.11 s on average for inquiry (simple) and 1.07 s on average for inquiry (complex). This is consistent with the typical response times of the studies investigated in this paper. If CyCOP is developed in compliance with the framework items (UI, object symbol, object size, response speed) presented in this paper, rapid situational awareness is possible. This research can be used in cyber-attack and defense training in the military field. In the private sector, it can be used in cyber and network control.

As network technology evolves, cyberattacks are not only increasing in frequency but also becoming more sophisticated. To proactively detect and prevent these cyberattacks, researchers are developing intrusion detection systems (IDSs) leveraging machine learning and deep learning techniques. However, a significant challenge with these advanced models is the increased training time as model complexity grows, and the symmetry between performance and training time must be taken into account. To address this issue, this study proposes a fast-persistent-contrastive-divergence-based deep belief network (FPCD-DBN) that offers both high accuracy and rapid training times. This model combines the efficiency of contrastive divergence with the powerful feature extraction capabilities of deep belief networks. While traditional deep belief networks use a contrastive divergence (CD) algorithm, the FPCD algorithm improves the performance of the model by passing the results of each detection layer to the next layer. In addition, the mix of parameter updates using fast weights and continuous chains makes the model fast and accurate. The performance of the proposed FPCD-DBN model was evaluated on several benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS-2017. As a result, the proposed method proved to be a viable solution as the model performed well with an accuracy of 89.4% and an F1 score of 89.7%. By achieving superior performance across multiple datasets, the approach shows great potential for enhancing network security and providing a robust defense against evolving cyber threats.

KSII Transactions on Internet and Information Systems TIIS Paper Details

As the world undergoes rapid digitalization, individuals and objects are becoming more extensively connected through the advancement of Internet networks. This phenomenon has been observed in governmental and military domains as well, accompanied by a rise in cyber threats consequently. The United States (U.S.), in response to this, has been strongly urging its allies to adhere to the RMF standard to bolster the security of primary defense systems. An agreement has been signed between the Republic of Korea and the U.S. to collaboratively operate major defense systems and cooperate on cyber threats. However, the methodologies and tools required for RMF implementation have not yet been fully provided to several allied countries, including the Republic of Korea, causing difficulties in its implementation. In this study, the U.S. RMF process was applied to a specific system of the Republic of Korea Ministry of National Defense, and the outcomes were analyzed. Emphasis was placed on the initial two stages of the RMF: ‘system categorization’ and ‘security control selection’, presenting actual application cases. Additionally, a detailed description of the methodology used by the Republic of Korea Ministry of National Defense for RMF implementation in defense systems is provided, introducing a keyword-based overlay application methodology. An introduction to the K-RMF Baseline, Overlay, and Tailoring Tool is also given. The methodologies and tools presented are expected to serve as valuable references for ally countries, including the U.S., in effectively implementing the RMF. It is anticipated that the results of this research will contribute to enhancing cyber security and threat management among allies.

This paper proposes a standards-aligned hybrid artificial intelligence–digital twin (DT) framework for predictive maintenance (PdM) in the maritime domain under conditions of data scarcity and heterogeneous sensor environments. The proposed framework adopts a DT-ready reference architecture centered on an ISO 19848-aligned data contract enabling consistent signal naming across vessels and equipment. On this foundation, the prognostics module is designed as a Domain-Knowledge Enhanced LSTM (DK-LSTM), a constraint-regularized sequence model in which three domain-informed constraints—(i) RUL non-negativity, (ii) monotonic degradation, and (iii) operating-range upper bounds—are formulated within the learning objective. Constraints (i) and (iii) are active throughout, while constraint (ii) is reserved for future work due to the structural limitation of batch-sort approximation in single-output architectures. An asymmetric safety penalty further suppresses hazardous over-predictions. Scenario-based virtual experiments are conducted using the NASA C-MAPSS turbofan degradation benchmark, evaluated under (1) sensor missingness via masking indicators and (2) structural domain shift comprising operational-condition shift (E3a: FD001 → FD002) and fault-mode shift (E3b: FD001 → FD003). Through systematic ablation of loss weights and stabilization techniques across multi-seed verification (seeds 0, 42, 123), the final stabilized configuration (DK-LSTM-v4) demonstrates robust safety-critical prediction in zero-shot domain-shift scenarios: 43.7% NASA Score improvement over the strongest baseline (GRU) under E3a and 20.8% improvement under E3b. The model trades modest in-domain performance for substantial cross-domain robustness, aligning with the core requirement of safety-critical maritime and defense applications where target-domain training data is unavailable.

Most of the challenges and conflicts facing countries and groups today involve cyberspace. Therefore, military forces around the world are developing methods (doctrines) and weapon systems to conduct cyberspace operations in order to dominate cyberspace. To conduct cyberspace operations, cyber target information must be collected in cyberspace, and cyber targets must be selected to achieve effective operational objectives. In this study, we develop a target importance rank (TIR) algorithm based on the PageRank algorithm that quantitatively calculates the importance of each target in cyberspace for cyber target selection. The proposed algorithm was developed to quantify the degree of connectivity, criticality, and exposure of computer network hosts, and to create a list of target importance priorities based on the quantified values.