Security Assessment Research Articles

Water security assessment and prediction for coastal Urban Agglomeration: a case study in the Pearl River Delta, east China

Water security assessment and prediction for coastal Urban Agglomeration: a case study in the Pearl River Delta, east China

Italian Evaluation and Excellence in REMS (ITAL-EE-REMS): appropriate placement of forensic patients in REMS forensic facilities

BackgroundWe set out to assess the appropriateness of current placement of mentally disordered offenders allocated by the courts in Italy to REMS or to forensic community residences. We hypothesised that as in other countries, the match between a standardised assessment and the decision of the court would be imperfect.MethodsThe DUNDRUM Toolkit was translated into Italian. The translation had good psychometric properties. In order to compare the current level of therapeutic security with a calculated safest current placement, we compared the DUNDRUM-1 triage security assessment of need for therapeutic security prior to treatment, with evidence for progress made in treatment (DUNDRUM-3) and forensic recovery (DUNDRUM-4). The more conservative of these two would be taken as the safe current level of need for therapeutic security.ResultsThe Italian translation of the DUNDRUM Toolkit had good internal consistency and mean scores had a Reliable Change Index less than one unit. 3.7% of those in REMS (medium security) were assessed as needing high security and 38% were ready to move to a less secure place. In low secure places, 56% were assessed as needing a higher level of therapeutic security and 6% could have moved to open non-secure places.ConclusionsThe Italian translation of the DUNDRUM Toolkit allows an assessment of the current working of the model of care for forensic psychiatry following the reforms of 2015. Most patients are safely placed. A small but important proportion needed high secure places that are not currently available. (3.7% of 604 nationally, 95% Confidence Interval 1.2% to 8.4%, 7 to 50). A greater use of such measures would enable better health gains and safer outcomes.Trial registration ClinicalTrials.gov ID: NCT06018298 Unique Protocol ID: ITAL-EE-REMS.

A bibliometric study toward quantitative research assessment of security of machine learning

Purpose Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems against cyberattacks. This paper aims to report findings from a quantitative analysis of literature within ML security to assess current research trends in ML security. Design/methodology/approach The study focuses on statistical analysis of literature published between 2000 and 2023, providing quantitative research contributions targeting authors, countries and interdisciplinary studies of organizations. This paper reports existing surveys and a comparison of publications of attacks on ML and its in-demand security. Furthermore, an in-depth study of keywords, citations and collaboration is presented to facilitate deeper analysis of this literature. Findings Trends identified between 2021 and 2022 highlight an increase in focus on adversarial ML – 40\% more publications compared to 2020–2022 with more than 90\% publications in journals. This paper has also identified trends with respect to citations, keywords analysis, annual publications, co-author citations and geographical collaboration highlighting China and the USA as the countries with highest publications count and Biggio B. as the researcher with collaborative strength of 143 co-authors which highlight significant pollination of ideas and knowledge. Keyword analysis highlighted deep learning and computer vision as the most common domains for adversarial attacks due to the potential to perturb images whilst being challenging to identify issues in deep learning because of complex architecture. Originality/value The study presented in this paper identifies research trends, author contributions and open research challenges that can facilitate further research in this domain.

Online Dynamic Security Assessment: Using Hybrid Physics-Guided Deep Learning Models

Online Dynamic Security Assessment: Using Hybrid Physics-Guided Deep Learning Models

Supply-demand Security Assessment of Water-energy-food Systems: A Perspective on Intra-city Coupling and Inter-city Linkages of Ecosystem Services

Ensuring the supply-demand security of water-energy-food systems (WEF) is paramount in sustainable cities. Leveraging ecosystem services (ESs) as a bridge between WEF supply and demand, this study proposes a conceptual framework for assessing the supply-demand security of WEF from the perspective of intra-city coupling and inter-city linkages of WEF-related ESs. Considering 14 Liaoning cities, we developed supply-demand indices for three ESs: water yield, carbon storage, and food production. The supply-demand security pattern of WEF was evaluated using Spearman's rank correlation, the Copula model, the coupling coordination degree model, the gravity model, and social network analysis, using these indices. The results show that the supply-demand security of WEF was higher in eastern Liaoning cities and weakened westward. Dandong had the highest supply-demand security, with a 98% probability of achieving moderate surplus in WEF resources. The regions of Dandong-Liaoyang-Anshan-Yingkou-Panjin-Jinzhou and Tieling-Fushun-Benxi formed two extreme WEF coupling coordination gravity networks. Liaoyang, Panjin, and Fuxin emerged as hubs in the WEF coupling coordination gravity networks and exhibited the highest degree and betweenness centrality values. Additionally, Fushun, Liaoyang, Dandong, and Tieling were identified as WEF high coupling coordination nodes. This supply-demand security assessment framework for WEF offers a scientific basis for developing sustainable city strategies.

Improving Data Security in Cloud Computing through the Implementation of a Hybrid Encryption Algorithm

As cloud computing continues to gain popularity, the security of sensitive data stored in the cloud remains a paramount concern for organizations and individuals alike. Traditional encryption methods, while effective, often struggle to balance security, efficiency, and usability. This paper presents a hybrid encryption algorithm that combines the strengths of symmetric and asymmetric encryption to enhance data security in cloud computing environments. The proposed hybrid approach leverages symmetric encryption for fast and efficient data encryption, while asymmetric encryption is utilized for secure key exchange. By implementing this dual-layer encryption strategy, we can significantly improve data confidentiality and integrity during transmission and storage. Additionally, the algorithm incorporates robust key management practices, ensuring that encryption keys are safeguarded against unauthorized access. Through a series of performance evaluations and security assessments, we demonstrate that our hybrid encryption algorithm not only provides superior security but also maintains acceptable levels of performance, making it suitable for real-time applications. The results indicate that this approach effectively mitigates risks associated with data breaches and unauthorized access, positioning it as a viable solution for enhancing data security in cloud computing. This research contributes to the ongoing discourse on cloud security, offering a practical framework for organizations to protect their sensitive information in increasingly complex digital landscapes.

Spatial deformation prediction method of fractured rock tunnel based on quantified GSI and its application

Determining rock mass mechanical parameters and accurately predicting tunnel deformation during tunnel construction remain challenging tasks. This study introduces a novel approach to calculate the Geological Strength Index by integrating indoor rock mechanics tests with geometric data from three-dimensional dense reconstruction. We utilized the Hoek-Brown strength criterion to develop a theoretical model for predicting tunnel deformation in fractured rock masses. Case studies reveal that the average value of Geological Strength Index for the Xiamen highway tunnel’s surrounding rock is 44. With support from the lining structure equivalent to 0.002–0.02 times the initial in-situ stress, the plastic zone thickness decreased by approximately 50%, and radial displacement was reduced by about 40%. Enhancing the lining structure’s support pressure significantly reduces the plastic zone radius and radial displacement. As the Geological Strength Index decreases, the nonlinearity between support pressure and plastic zone radius becomes more pronounced, with a similar trend observed for the relationship between support pressure and tunnel radial displacement. The relative deviation between predicted and measured values did not exceed 1.69%. The method accurately captures the effects of rock fragmentation and tunnel construction on plastic zone formation and displacement, offering an effective approach for the rapid and secure assessment of rock tunnel excavation stability using digital technology.

Decision Support Model for Selecting the Optimal Blockchain Oracle Platform: An Evaluation of Key Factors

Smart contract-based applications are executed in a blockchain environment, and they cannot directly access data from external systems, which is required for the service provision of these applications. Instead, smart contracts use agents known as blockchain oracles to collect and provide data feeds to the contracts. The functionality and compatibility with smart contract applications need to be considered when selecting the best-fit oracle platform. As the number of oracle alternatives and their features increases, the decision-making process becomes increasingly complex. Selecting the wrong or sub-optimal oracle is costly and may lead to severe security risks. This paper provides a decision support model for the oracle selection problem. The model supports smart contract decision-makers in selecting a secure, cost-effective, and feasible oracle platform for their applications. We interviewed oracle co-founders and smart contracts experts to refine and validate the decision model. Two real-world smart contract application case studies were used to evaluate the model. Our model prioritises and suggests more than one possible oracle platform based on the developer's required criteria, security assessment, and cost analysis. Moreover, this guided decision model serves to reveal issues that may go unnoticed if done haphazardly, reduce decision-making efforts, and provide a cost-effective solution.

Assessment of energy security in West Africa: A case study of three countries

The study draws key energy policy lessons by assessing and comparing the energy security performance of Burkina Faso, Nigeria and Ghana. The Energy Security Index with application to West Africa is created from eight dimensions and 24 indicators using a simple additive method and non-statistical induced weights. Study results show that the main energy security challenges in West Africa are low public investment in energy infrastructure (including renewable energy), high energy prices, low electricity access, high energy system inefficiencies, armed attacks on energy infrastructure and deteriorating energy sector governance. The study recommends prioritising investment in electricity sector infrastructure in West Africa.

Demystification of Readiness, Security, and Technological Enhancements in the Adoption of a Cashless Economy

The adoption of a cashless economy was accelerated globally by the devastating impact of the COVID-19 pandemic. Brunei Darussalam was not excluded from this trend, as pandemic-related restrictions were implemented to ensure the safety of its population. In light of the COVID-19 crisis, this research paper examines the factors influencing the readiness and acceptance of a cashless economy among working society in Brunei Darussalam. The integrated concepts of the Technology Acceptance Model (TAM) and Technology Readiness Index (TRI) are applied to examine perceptions impacting their acceptance and readiness to continue adopting a cashless economy. The methodology includes a literature review and the use of secondary data from government reports and industry publications. A quantitative approach is employed, utilizing an online survey to collect non-probability samples from 212 respondents. The main instruments used in the survey are structured questionnaires. The study’s findings show that factors such as the assessment of payment modes, technological development, digital literacy, knowledge, regulatory policies, and security concerns significantly affect working society’s perceptions, readiness, and acceptance of a cashless economy. These results provide insights for policymakers and stakeholders on the key factors influencing continued cashlessness adoption and shaping societal behavior towards cashless payments.

Artificial intelligence techniques for dynamic security assessments - a survey

The increasing uptake of converter-interfaced generation (CIG) is changing power system dynamics, rendering them extremely dependent on fast and complex control systems. Regularly assessing the stability of these systems across a wide range of operating conditions is thus a critical task for ensuring secure operation. However, the simultaneous simulation of both fast and slow (electromechanical) phenomena, along with an increased number of critical operating conditions, pushes traditional dynamic security assessments (DSA) to their limits. While DSA has served its purpose well, it will not be tenable in future electricity systems with thousands of power electronic devices at different voltage levels on the grid. Therefore, reducing both human and computational efforts required for stability studies is more critical than ever. In response to these challenges, several advanced simulation techniques leveraging artificial intelligence (AI) have been proposed in recent years. AI techniques can handle the increased uncertainty and complexity of power systems by capturing the non-linear relationships between the system’s operational conditions and their stability without solving the set of algebraic-differential equations that model the system. Once these relationships are established, system stability can be promptly and accurately evaluated for a wide range of scenarios. While hundreds of research articles confirm that AI techniques are paving the way for fast stability assessments, many questions and issues must still be addressed, especially regarding the pertinence of studying specific types of stability with the existing AI-based methods and their application in real-world scenarios. In this context, this article presents a comprehensive review of AI-based techniques for stability assessments in power systems. Different AI technical implementations, such as learning algorithms and the generation and treatment of input data, are widely discussed and contextualized. Their practical applications, considering the type of stability, system under study, and type of applications, are also addressed. We review the ongoing research efforts and the AI-based techniques put forward thus far for DSA, contextualizing and interrelating them. We also discuss the advantages, limitations, challenges, and future trends of AI techniques for stability studies.

THE INDICATOR APPROACH TO ASSESSING THE ECONOMIC SECURITY OF ENTERPRISES

In the conditions of market instability, competitive pressure, financial crises and other external and internal challenges, an important component of the stability and development of the enterprise is economic security, which is the subject of research in the article. It was found that the assessment of economic security helps to adapt to the existing conditions and gives the ability to quickly eliminate various threats, which ensures the stable operation of the enterprise. The study of literature sources revealed that there are many methods for assessing the economic security of enterprises, but one of the most effective is the indicator approach. This approach is based on the analysis and interpretation of a number of key indicators that allow determining the state of various areas of the enterprise. The indicator approach is a systematic approach to assessing economic security by analyzing a set of indicators that reflect the state of the enterprise in key areas of its activity. Indicators are quantitative indicators that characterize the state of individual spheres of the enterprise's functioning and allow to evaluate them as a whole. The indicator approach is universal and can be adapted to the specifics of various industries and enterprises. This makes it an effective tool for a wide range of enterprises, regardless of their size, structure or field of activity. The assessment of economic security through indicators allows enterprise managers to make more accurate and well-founded management decisions, which contributes to strengthening their resistance to external and internal challenges. The application of the indicator approach not only increases the sustainability of the enterprise in the short term, but also contributes to the development of long-term strategies that take into account both current and future risks. The indicator approach allows for a comprehensive and systematic assessment of the state of economic security and covers key aspects of the enterprise's activity - financial, production, personnel, legal and other areas. This provides a deep understanding of the current state of the enterprise and its vulnerabilities. Depending on the significance of the threats and the subjective preferences of the authors and the specifics of the industry, the choice and method of grouping indicators for assessing the level of economic security of the enterprise differ significantly.

An intuitive approach to cybersecurity risk assessment for non-governmental organizations

Purpose This study proposes a guided tool for cybersecurity risk assessment tailored for nongovernmental organizations (NGOs), enabling them to comply with cybersecurity policies despite limitations in security awareness, funding and expertise. Design/methodology/approach A digital transformation is indispensable for ensuring the sustainable operation of NGOs. Embracing a digital manifesto necessitates an awareness of cybersecurity risks, highlighting the critical need for a robust cybersecurity risk assessment methodology. Initial research phases revealed significant shortages in security awareness, funding and expertise. Consequently, this study introduces an intuitive approach tailored specifically for NGOs, supported by a customized tool designed to address their unique requirements. The NIST cybersecurity risk assessment framework and National Cyber-security Authority (NCA) were adopted to define the risk assessment approach. The efficacy of this approach is evaluated qualitatively through a case study involving three NGOs in Saudi Arabia, aimed at assessing their capability to utilize the tool effectively. Following the implementation, a Likert-scale survey gauged satisfaction among NGOs regarding the tool’s utility. Findings Results from the case study indicate high satisfaction, affirming its alignment with their operational needs and enhancement of compliance with NCA controls. Furthermore, the use of the tool enhances the awareness of NCA’s cybercity requirements and controls. Originality/value Based on theoretical and empirical grounds, this research proposes a novel design of security assessment framework tailored for NGO requirements and supported by initiative tool enabling complying with cybersecurity policies and enhances the awareness of cybersecurity controls.

Assessment of Future Water Security under Climate Change: Practical Water Allocation Scenarios in a Drought-Prone Watershed in South Korea

Seomijn River Basin has numerous hydraulic structures designed to satisfy water demands and mitigate future droughts. However, the increasing water demand and export to neighboring areas cause water deficits and conflicts between water users. Therefore, practical strategies to mitigate the potential damage from climate change are essential. In this study, we aimed to propose practical strategies under climate change by examining the future water security of the Seomjin River Basin under five different water allocation scenarios referenced from the practical policies of various countries. Future climate models determined based on extreme precipitation indices of the ETCCDI were used to investigate their impact on water security, which was evaluated using unmet demand; demand coverage; reliability, resilience, and vulnerability; and aggregation index metrics. We found that prioritizing domestic and industrial water use is the optimal water security strategy, and unconditional allocation of instream flow can cause a significant water deficit for other water uses. However, prioritizing all water uses equally also proved effective under some conditions. Thus, our study highlights the importance of adaptive management and suggests that the optimal water allocation strategy lies in its flexibility in response to varying circumstances.

Development of a Cloud Computing-Based Collaboration Information System

The rapid advancement of information and communication technology has created a demand for efficient and effective information systems to facilitate collaboration among institutions. This study develops a cloud computing-based collaboration information system designed to enhance real-time communication, data management, and collaboration among users. The system comprises key modules, including collaboration management, document management, and integrated collaboration features. Testing involved various scenarios with 10 to 100 active users simultaneously, revealing that the system maintained stable performance with response times under 3 seconds for each user request. Furthermore, security assessments utilizing encryption protocols and two-factor authentication confirmed that the managed data is protected from external threats. The findings underscore the system's potential to improve collaborative efficiency while ensuring data security, making it a valuable solution for future collaborative initiatives.

New Fermatean Fuzzy Distance Metric and Its Utilization in the Assessment of Security Crises Using the MCDM Technique

The problem of insecurity is a global phenomenon that has several forms like terrorism, banditry, kidnappings, etc. Insecurity has taken hold in the Sub-Saharan Region of West Africa, especially in Nigeria, for over two decades. Nigeria’s security crisis is more pronounced in the Northern Region, with a new wave in the North-Central Region of Nigeria. It is herculean to assess insecurity in the North-Central Region of Nigeria because of the region’s fuzzy or imprecise nature of insecurity. This constitutes the rationale for deploying the Fermatean fuzzy technique to assess insecurity due to the capacity of the Fermatean fuzzy scheme to handle imprecision. To this end, a new Fermatean fuzzy distance metric is presented to evaluate insecurity in the North-Central Region of Nigeria using a multi-criteria decision-making technique. To express the logic for creating the new Fermatean fuzzy distance metric, some existing Fermatean fuzzy distance metrics are discussed, along with their drawbacks. The mathematical properties of the new technique are discussed, and the new method is applied computationally to assess insecurity in the North-Central Region of Nigeria. The data for the security assessment are collected via Fermatean fuzzy linguistic variables using the opinions of security experts and analyzed using the technique for order of preference by similarity to ideal solution, which is a commonly used multi-criteria decision-making method. Finally, the numerical validity of the new technique is expressed with comparative results, and the finding shows the benefit of the new distance approach over the existing methodologies. The outcome of the work will provide reliable traveling advisories for safe voyages within the region.

Data-Driven Dynamic Security Partition Assessment of Power Systems Based on Symmetric Electrical Distance Matrix and Chebyshev Distance

A rapid dynamic security assessment (DSA) is crucial for online preventive and restoration decision-making. The deep learning-based DSA models have high efficiency and accuracy. However, the complex model structure and high training cost make them hard to update quickly. This paper proposes a dynamic security partition assessment method, aiming to develop accurate and incrementally updated DSA models with simple structures. Firstly, the power grid is self-adaptively partitioned into several local regions based on the mean shift algorithm. The input of the mean shift algorithm is a symmetric electrical distance matrix, and the distance metric is the Chebyshev distance. Secondly, high-level features of operating conditions are extracted based on the stacked denoising autoencoder. The symmetric electrical distance matrix is modified to represent fault locations in local regions. Finally, DSA models are constructed for fault locations in each region based on the radial basis function neural network (RBFNN) and Chebyshev distance. An online incremental updating strategy is designed to enhance the model adaptability. With the simulation software PSS/E 33.4.0, the proposed dynamic security partition assessment method is verified in a simplified provincial system and a large-scale practical system in China. Test results demonstrate that the Chebyshev distance can improve the partition quality of the mean shift algorithm by approximately 50%. The RBFNN-based partition assessment model achieves an accuracy of 98.96%, which is higher than the unified assessment with complex models. The proposed incremental updating strategy achieves an accuracy of over 98% and shortens the updating time to 30 s, which can meet the efficiency of online application.

Assessment and forecasting of water ecological security and obstacle factor diagnosis in the Hexi Corridor of Northwest China

The water ecological security pattern is a core factor. A scientific, accurate, and practical evaluation of water ecological security provides a theoretical basis for regional water ecological management. Using water resource data from five cities in the Hexi Corridor of Gansu Province (Jiuquan (JQ), Jiayuguan (JYG), Zhangye (ZY), Jinchang (JC), and Wuwei (WW)) from 2006 to 2021, a water ecological security evaluation index system based on the PSR (pressure-state-response) framework was constructed, covering 27 factors related to water resources, socio-economics, and the ecological environment. The main obstacle factors of water ecological security were identified using the obstacle degree model, and the grey GM(1,1) model was employed to predict water ecological security. Results indicated that the comprehensive assessment index of water ecology in the Hexi Corridor increased from 2006 to 2021, showing a transition from relatively unsafe (0.319) to basic safety and then to relatively safe (0.672). The pressure and response systems were the main limiting factors affecting water ecological security in the Hexi Corridor. After a slight decline in 2008, the overall spatial distribution continued to rise, with WW City and ZY City leading since 2016. ZY had a higher safety grade proportion (25%) compared to other areas in the Hexi region. The pressure system was the most significant obstacle to water ecological security after 2006. Prediction results indicated that the comprehensive evaluation index of water ecological security would continue to rise annually from 2022 to 2031, reaching a very safe level by 2025. The evaluation results provide a scientific basis for ecological security and risk decision-making in the study area.

Strategic Evaluation of Whistleblower Software Security in Government: ISO/IEC 25010 and AHP Method

To assess the effectiveness of software security measures in government whistleblower systems, we will utilize the ISO/IEC 25010 standard and the Analytic Hierarchy Process (AHP) methodology. Through the integration of various frameworks, our objective is to build a complete evaluation model that effectively identifies and enhances any vulnerabilities in these crucial systems. The strategy we employ combines the qualitative and quantitative evaluation capabilities of ISO/IEC 25010 and AHP, respectively, to offer a comprehensive perspective on software security performance. The results indicate substantial improvements in the security and reliability of whistleblower software, underscoring the effectiveness of our suggested evaluation technique in identifying crucial areas for refinement. Moreover, the utilization of AHP permitted the ranking of security qualities, guaranteeing focused and efficient improvements. Ultimately, the study emphasizes the significance of thorough security assessments for government whistleblower systems and verifies the effectiveness of utilizing ISO/IEC 25010 and AHP as a methodical approach to improve software security. This research enhances the ongoing endeavor to protect confidential data, fostering a more secure and reliable atmosphere for individuals who expose wrongdoing.

Security Assessment Framework for DDOS Attack Detection via Deep Learning

Internet of Things (IoT) is a broad network of actual, physical objects that can exchange data and communicate with other devices and systems over the Internet. Due to the scalability of the internet, the number of IoT users is increasing, and therefore it is vital to ensure the security of user info. Attacks such as DDoS (Distributed Denial of Service) and MitM (Man-in-the-Middle) can seriously interrupt operations and threaten the confidentiality and integrity of sensitive data. In this paper, a novel Iot based Security Assessment for intrusion using Deep Learning (ISAI-DL) technique has been proposed to identify IoT device vulnerabilities such as DDoS and MitM attacks. Initially, the features are extracted from the API documents using Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques. In proposed ISAI-DL technique, it triggers or activates the vulnerabilities deep within target devices by sending API documents. The vulnerability is detected via the GloVe-CNN-BiLSTM Model; if the attack occurs in an IoT device, the output is classified as either DDoS, MitM, or normal. Evaluation metrics such as accuracy, recall, precision, time efficiency, F1 score, false alarm rate, and detection rate have been used to measure how effective the proposed ISAI-DL technique is. Based on a comparative investigation, the detection rate of the proposed ISAI-DL technique is 17.23%, 18.53%, and 3.24% greater than that of the existing P2ADF, HDA-IDS, and BT-TPF techniques respectively.