Network Risk Assessment Research Articles

Vulnerability module in the multi-level ontology of risk assessment of Wireless Sensor Networks

This work presents the development of a vulnerability module within a multi-level ontology for risk assessment in wireless sensor networks (WSNs) used in industrial environments. WSNs consist of multiple sensor nodes with microcontrollers, sensors, communication devices, and power sources, playing a key role in monitoring, diagnostics, and managing industrial processes. Their main advantages include reduced costs due to the lack of cabling and ease of scaling by adding new nodes. However, WSNs face significant challenges, including limited power supply, vulnerability to electromagnetic interference, low bandwidth, and exposure to cyber-attacks. These issues make them less suitable for real-time systems where fast and reliable data transmission is critical. The vulnerability module developed in this study addresses these challenges by identifying weaknesses, analyzing potential threats, and assessing risks using logical rules. These rules assess various network components such as devices, communication protocols, and external factors like physical access and radio interference. The module continuously monitors the network, detects new vulnerabilities, and provides real-time feedback to the risk assessment module, suggesting measures to mitigate risks. This enhances the security and reliability of WSNs in industrial applications. In conclusion, the vulnerability module within the multi-level ontology provides a structured approach to identifying and mitigating risks in WSNs. Despite the inherent vulnerabilities of WSNs, advancements in security protocols and energy efficiency make them increasingly viable for industrial automation and optimization.

Serviceability fragility assessment of non-seismically designed railway viaducts in Turkey under near-field and far-field earthquakes

While many railway viaducts still in use in Turkish railway networks are located on active fault zones that produced historical destructive earthquakes, they are not seismically designed in accordance to current standards. This study aims to provide a better understanding about the serviceability fragility of such systems by conducting detailed probability-based seismic assessments under near-field and far-field earthquakes. To achieve this, firstly, 3D finite element models of a range of selected viaducts in Turkey were generated based on their original design drawings. The developed FE models were validated by comparing the analytical modal frequencies with the results of in-situ dynamic tests involving a series of acceleration measurements. Nonlinear time history analyses were then carried out under 25 near-field and 25 far-field real three-component ground motion recordings to obtain the seismic response of each selected viaduct. Subsequently, probabilistic seismic demand models were defined using linear regression analysis to determine relationships between engineering demand parameters (EDPs) and ground motion intensity measures (IMs). Peak ground acceleration (PGA), peak ground velocity (PGV) and spectral acceleration at 1.0 s (Sa (1.0)) were used as the IM parameter, while the maximum lateral displacement of the bridge spans for different service velocities defined in the Eurocode was considered as the EDP at serviceability damage state. Finally, analytical fragility curves for all the selected railway viaducts were developed considering maximum damage probability for the IM levels. The results, in general, demonstrate the seismic vulnerability of the existing viaducts in Turkey. It is shown that while at low speed limits the viaducts exposed to far-field ground motions were more vulnerable than those under near-field ground motions, at high speed limits the viaducts subjected to near-field ground motions were more vulnerable. Also, it is seen that reinforced concrete and masonry viaducts are generally more vulnerable to earthquakes than the steel viaducts. The outcomes of this study should prove useful for the seismic risk assessment, loss estimation and rehabilitation of the railway transportation networks in future studies.

Quantifying and enabling the resiliency of a microgrid considering electric vehicles using a Bayesian network risk assessment

With the increase in the number of natural disasters, caused by climate change, the role of emission and resilience has become more outstanding. In this paper, a novel model for quantifying and enabling the resiliency of a microgrid, using a Bayesian network risk assessment approach is presented. The three objective functions of cost, pollution, and resilience are optimized simultaneously using an improved lexicographic augmented ɛ-constraint method. It should be mentioned that these items are included in the proposed model due to the irreplaceable role of electric vehicles (EVs) and renewable energy sources (RESs) in distribution systems. Moreover, with the Bayesian network, the interaction between random variables is considered and a graphic view of casual relationships between stochastic and probabilistic variables is investigated in this study. The Monte Carlo scenario-based technique has handled the uncertainties of EVs and RESs. The proposed mixed integer linear programming (MILP) is implemented in the GAMS software environment under the CPLEX solver and finally, the results are discussed.

The Vulnerability Relationship Prediction Research for Network Risk Assessment

The Vulnerability Relationship Prediction Research for Network Risk Assessment

Knowledge graph-based Bayesian network risk assessment model for hydrogen accidents

A Bayesian network risk assessment model for hydrogen accidents based on a knowledge graph is proposed. The study uses hydrogen accident report texts to form hydrogen accident knowledge texts by analyzing, processing, and extracting text fragments with transparent causal relationships. Then, the Bert-BilSTM-CRF algorithm is used to extract knowledge, and the results are stored in the Neo4j database to construct a knowledge map, obtain the risk factors of hydrogen accidents, and complete the structural modeling of the Bayesian network. The improved SAM method is used to aggregate expert opinions, calculate the prior and posterior probability, and realize the parameter learning of the Bayesian network. Based on a knowledge graph, this forms a Bayesian network risk assessment model for hydrogen accidents. This model can evaluate and warn against hydrogen accidents in a data-driven manner and promote knowledge acquisition, analysis, and decision-making on hydrogen energy storage and transportation risks.

Bayesian network prediction study on the impact of occupational health psychological factors on insomnia among thermal power generation workers

Objective: To explore the risk factors of insomnia among employees in the thermal power generation industry and the network relationships between their interactions, and to provide scientific basis for personalized interventions for high-risk groups with insomnia. Methods: In November 2022, 860 employees of a typical thermal power generation enterprise were selected as the research subjects by cluster sampling. On-site occupational health field surveys and questionnaire surveys were used to collect basic information, occupational characteristics, anxiety, depression, stress, occupational stress, and insomnia. The interaction between insomnia and occupational health psychological factors was evaluated by using structural equation model analysis and Bayesian network construction. Results: The detection rates of anxiety, depression and stress were 34.0% (292/860), 32.1% (276/860) and 18.0% (155/860), respectively. The total score of occupational stress was (445.3±49.9) points, and 160 workers (18.6%) were suspected of insomnia, and 578 workers (67.2%) had insomnia. Structural equation model analysis showed that occupational stress had a significant effect on the occurrence of insomnia in thermal power generation workers (standardized load coefficient was 0.644), and occupational health psychology had a low effect on insomnia (standardized load coefficient was 0.065). However, the Bayesian network model further analysis found that anxiety and stress were the two parent nodes of insomnia, with direct causal relationships, the arc strength was-8.607 and -15.665, respectively. The model prediction results showed that the probability of insomnia occurring was predicted to be 0 in the cases of no stress and anxiety, low stress without anxiety, and no stress with low anxiety. When high stress with low anxiety and low stress with high anxiety occurred, the predicted probability of insomnia occurring were 0.38 and 0.47, respectively. When both high stress and high anxiety occurred simultaneously, the predicted probability of insomnia occurring was 0.51. Conclusion: Bayesian network risk assessment can intuitively reveal and predict the insomnia risk of thermal power generation workers and the network interaction relationship between the risks. Anxiety and stress are the direct causal risks of insomnia, and stress is the main risk of individual insomnia of thermal power generation workers. The occurrence of insomnia can be reduced based on scientific intervention of stress conditions.

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

The intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber threats increase. The existing methods cannot make sequential decisions in complex network environments, particularly in scenarios with partial observability and non-stationarity. Network awareness monitors and comprehends the network's assets, vulnerabilities, and ongoing activities in real-time. Advanced analytics, machine learning algorithms, and artificial intelligence are used to improve risk perception by analyzing massive amounts of information, identifying trends, and anticipating future security breaches. Hence, this study suggests the Deep Reinforcement Learning-assisted Network Awareness Risk Perception and Prevention Model (DRL-NARPP) for detecting malicious activity in cybersecurity. The proposed system begins with the concept of network awareness, which uses DRL algorithms to constantly monitor and evaluate the condition of the network in terms of factors like asset configurations, traffic patterns, and vulnerabilities. DRL provides autonomous learning and adaptation to changing network settings, revealing the ever-changing nature of network awareness risks in real time. Incorporating DRL into risk perception increases the system's capacity to recognize advanced attack methods while simultaneously decreasing the number of false positives and enhancing the reliability of risk assessments. DRL algorithms drive dynamic and context-aware response mechanisms, making up the adaptive network prevention component of the development. Predicting new threats and proactively deploying preventive measures, such as changing firewall rules, isolating compromised devices, or dynamically reallocating resources to reduce developing risks, is made possible by the system's ability to learn from historical data and prevailing network activity. The suggested DRL-NARPP model increases the anomaly detection rate by 98.3%, the attack prediction accuracy rate by 97.4%, and the network risk assessment ratio by 96.4%, reducing the false positive ratio by 11.2% compared to other popular methodologies.

Distribution network risk assessment with distributed generation impact based on non-sequential Monte Carlo method

At present, the development of renewable energy distributed generation is fast, and the distribution network is facing significant challenges. Unlike traditional fossil fuels, new energy generation is characterized by significant volatility caused by uncertain factors like environmental and meteorological conditions. This paper presents a risk assessment index system that utilizes the non-sequential Monte Carlo method to tackle the given issue. The system comprises two assessments: voltage limit risk and reliability index assessment. Through the application of the Monte Carlo method, a comparative analysis of the results with alternative calculation approaches is conducted. Subsequently, these findings are utilized to evaluate the effectiveness of two commonly employed protection strategies.

Expansion of the Fertility Preservation Program to All Newly Diagnosed Prepubertal Patients with Cancer at a Pediatric Hospital.

Background: As the majority of pediatric patients with cancer survive their disease, generating a population of over 500,000 childhood cancer survivors in the United States, it is imperative to minimize the lifelong consequences of treatment, which include temporary or permanent infertility caused by certain cancer treatments. A fertility consultation at diagnosis can provide patients and families with the opportunity to be informed regarding the likelihood of gonadal dysfunction and to consider fertility preservation. Method: After our pediatric hospital started to offer tissue cryopreservation, we initiated this evidence-based interventional quality improvement project. Our primary aim was to ensure that all newly diagnosed prepubertal patients with cancer who met the criteria for fertility tissue preservation were correctly identified and offered an educational consultation and preservation. Results: Between July 15, 2022 and October 30, 2022, 54 patients' treatment plans were evaluated to determine treatment-related infertility risk using the Oncofertility Consortium Pediatric Initiative Network's Risk Assessment tool. Fifteen patients were at a high level of significantly increased risk and 13 were eligible for consultation. Seven (46%) patients and their families received a consultation. Initiation of treatment before referral was the primary reason for lack of consultation. Six of seven patients receiving consultation (86%) elected to undergo preservation. Preservation procedures did not cause a delay in starting treatment for those patients. Discussion: A fertility preservation program with established policies and processes can increase the likelihood that prepubertal patients at high risk for infertility are correctly identified, educated, and offered preservation.

A fuzzy Bayesian network risk assessment model for analyzing the causes of slow-down processes in two-stroke ship main engines

ABSTRACT This paper presents a risk assessment approach for analyzing the causes of malfunction-related main engine slowdowns. A fuzzy Bayesian Network-based methodology is used to assess the factors contributing to the engine’s slow-down processes. The model addresses the complexity and uncertainty inherent in maritime operations with fuzzy sets where numerous interrelated factors can affect engine performance, and the Bayesian network to capture probabilistic dependencies. It considers various potential causes of the slow-down of ship engines that the manufacturer provides. Results demonstrate the model's ability to identify the influential factors leading to engine slow-down events and quantify the overall risk. Integrating fuzzy logic and Bayesian Networks comprehensively assesses relevant risk factors. It enables maritime stakeholders to manage engine performance and improves operational safety proactively. Findings can inform decision-makers, enabling the implementation of targeted maintenance strategies, fuel quality control measures, and crew training programs in the maritime industry.

A comprehensive framework for quantitative risk assessment of organizational networks using FAIR-modified attack trees

Attack trees are a widely used method for threat modeling and analyzing cyber-attacks in organizational networks. Assessing the risk associated with each individual node of an attack tree is crucial for understanding the overall risk of the attack. This article presents a comparative study of different threat modeling methods and risk assessment approaches in organizational networks. The article also presents a novel comprehensive approach for quantifying risk assessment of organizational networks based on attack trees modified according to the factor analysis of information risk (FAIR) approach. Our results demonstrate the effectiveness of the novel approach in capturing the unique characteristics of different assets and their dependencies in an attack tree, leading to quantitative risk assessment.

Dynamic Real-Time Analysis of Network Attacks Based on Dynamic Risk Probability Algorithm

With the rapid development of Internet technology and its application, the existence of network vulnerabilities is very common. Attackers may use the defects of software, hardware, or system security policy in the network system to access or destroy the system without authorization. How to nip in the bud and carry out a safety risk assessment and early warning is an urgent problem to be solved. Based on the overall assessment of the risk factors in the whole network, the more dangerous nodes are found and priority measures are taken. The method proposed in this paper can reflect and predict the actions of attackers, repair, and adjust the previously predicted probability. It is compared with the method that evaluates the uncertainty in the network solely by calculating the static probability. The proposed new ideas and methods better reflect the real-time changes in the actual environment of the Internet, thereby better responding to the actual situation. This method can be well applied to threat detection, threat analysis, and risk assessment of monitoring system networks, enabling monitoring network managers to evaluate and protect the security of real-time power grids. It is of great significance to effectively defend against network attacks, ensure system security, and study the resistance of control systems under network attacks.

Simulation-based seismic risk and robustness assessment of aging bridge networks

Simulation-based seismic risk and robustness assessment of aging bridge networks

A Compendium on Risk Assessment of Phishing Attack Using Attack Modeling Techniques

With the advent of new attacking methodologies and types of attacks, it has become difficult to protect the systems. Therefore, it is necessary to decipher the impact of attacks before and after its occurrence to provide better security. It is hard to speculate future attacks, without understanding the network vulnerability and therefore, it is important to examine the network for identifying potential vulnerabilities using attack modeling. Phishing attacks is one of the prominent network attacks that pose continual threat to the network and organization. This article presents and discusses in brief attack modeling techniques that can be applied for risk assessment. The paper presents in detail modeling of phishing attack using attack graphs, cyber kill chain, diamond model, security incident response matrix, and data description model. Moreover, the article also discusses role of network defenders and system engineers in assessing risk and how attack models can be helpful to them for modeling attacks. The review presented in this paper can serve as a platform for researchers in the field of network security and risk assessment for developing effective and secure network environment.

Research progress on ecology and sustainable development of Guilin Lijiang River Basin, China, based on bibliometric analysis.

Guilin is a typical representative of karst landform in South China. Because of its unique geographical landform and hydrological environment, Lijiang River Basin has received a certain degree of attention in field of ecology and sustainable development. Explore and visualize the hotspots and frontiers of Guilin Lijiang River Basin Ecology and Sustainable Development (GLRBESD) by using bibliometrics, CiteSpace, and VOSviewer. Results showed that number of published papers was in a fluctuating upward trend from 1992 to 2022 and from 2011 to 2022, respectively. Work of scholars in this field has been continuously strengthened and deepened, and overall scientific research results show an increasing trend. Research objects and topics are mainly aimed at the water resources, climate, and environment of GLRB Landscape ecology and SDG index construction. Research of GLRBESD-published documents has the characteristics of multi-disciplinary and interdisciplinary integration. High-frequency keywords in research field focus on ecotourism, ecological restoration, and sustainable development, mainly based on the research of ecotourism development. Impact of environmental factor changes and human activities on land use change in different periods is an important research topic. Core research field of GLRBESD on macro-scale can be divided into ESV and function, ecological compensation and ecotourism, ecological environment and ecological restoration, ecological network and ecological risk assessment, and sustainable development. This research provides systematic scientific research basis for enhancing sustainable development ability and ecosystem functions and services of World Natural Heritage Site.

Viewpoints on Robust Supply Chain Network Risk Assessment Using Dynamic Bayesian Networks

The text dissects the exploration of employing dynamic Bayesian networks (DBNs) for reliably assessing the risk posed to supply chain networks. A growing recognition of the complexity and intricacy of contemporary supply chain networks has sparked interest in novel methodologies such as DBNs that can model temporal and spatial dependencies effectively. DBNs emerge as a powerful analytical tool enabling probabilistic inference under prevailing uncertainties, capturing both the inherent and external risks. The paper demonstrates how these networks, bestowed with both temporal and causal mathematical frameworks, offer superior analytical traction over traditional methods in understanding the interconnected risk elements. We discuss their ability to appraise the ever-changing vulnerabilities in a supply chain setting, dynamically adapting to modifications brought by stakeholder actions or market changes. As supply chains burgeon in complexity, the role of DBNs in enabling a robust, comprehensive understanding of risk factors becomes increasingly paramount. Thus, the paper advocates for the DBNs' application as a transformative approach to risk assessment, providing significant foresight and adaptability in an unpredictable supply chain landscape.

Evolution of global grain trade network and supply risk assessment based on complex network

Evolution of global grain trade network and supply risk assessment based on complex network

Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China

Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management.

A Dynamic Bayesian Network Probability Framework for Typhoon Risk Analysis in the Land-sea Regions

The life cycle of a typhoon is mainly divided into three stages: far away from the coast, offshore, and on land. The disaster bodies and typhoons affected are different at every stage. The traditional assessment methods could not consider the risk from the complete typhoon life cycle to cover the effects of both land and sea. This paper proposes a Bayesian network risk assessment model to evaluate the navigational ship risk and damage risk of coastal structures during the different typhoon life cycles. The disaster memory database of the typhoon is also constructed in this paper, which deconstructs the typhoon risk factors and forms a unified description semantics.

Node Vulnerability-Aware co-deployment of D-PMUs and FTUs for active distribution networks

This study proposes a co-deployment strategy of distribution-level phasor measurement units (D-PMUs) and feeder terminal units (FTUs) for active distribution networks (ADNs) considering node vulnerability to efficiently monitor and perceive the operational state of ADNs. The proposed measurement deployment strategy preferentially configures measurement devices with high precision and superior monitoring functions on vulnerable nodes while ensuring state estimation (SE) accuracy and cost economy. Based on complex network theory and risk assessment, a vulnerability index reflecting the “bridge” characteristics of nodes was defined. Considering the system monitorability, SE accuracy, and measurement cost, a measurement co-deployment strategy based on a combination weighting method was developed. An improved discrete particle swarm optimization algorithm was proposed to solve the co-deployment model by establishing a new particle coding format, improving the dynamic inertia weight and learning factors, and introducing chaos theory. Simulation results based on the IEEE 33-bus and 119-bus distribution systems verify the effectiveness and show that the proposed strategy not only improves system monitorability but also enhances SE robustness.