Identifying and mitigating security risks for secure and robust NGI networks

Lever, Kirsty E; Kifayat, Kashif

doi:10.1016/j.scs.2020.102098

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…It is also of importance and concern that in the current era of advanced technology, AI systems are getting more interconnected to cybersecurity due to the advancement of hardware and software (Mengidis et al, 2019). This has raised significant concerns regarding the security of various stakeholders and emphasizes the procedures the policymakers must adopt to prevent or minimize the threat (ELever and Kifayat, 2020). It is also important to note that security concerns increase with network and endpoints in remote learning.…”

Section: Theoretical Discussionmentioning

confidence: 99%

Impact of artificial intelligence on human loss in decision making, laziness and safety in education

Ahmad,

Han,

Alam

et al. 2023

Humanit Soc Sci Commun

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This study examines the impact of artificial intelligence (AI) on loss in decision-making, laziness, and privacy concerns among university students in Pakistan and China. Like other sectors, education also adopts AI technologies to address modern-day challenges. AI investment will grow to USD 253.82 million from 2021 to 2025. However, worryingly, researchers and institutions across the globe are praising the positive role of AI but ignoring its concerns. This study is based on qualitative methodology using PLS-Smart for the data analysis. Primary data was collected from 285 students from different universities in Pakistan and China. The purposive Sampling technique was used to draw the sample from the population. The data analysis findings show that AI significantly impacts the loss of human decision-making and makes humans lazy. It also impacts security and privacy. The findings show that 68.9% of laziness in humans, 68.6% in personal privacy and security issues, and 27.7% in the loss of decision-making are due to the impact of artificial intelligence in Pakistani and Chinese society. From this, it was observed that human laziness is the most affected area due to AI. However, this study argues that significant preventive measures are necessary before implementing AI technology in education. Accepting AI without addressing the major human concerns would be like summoning the devils. Concentrating on justified designing and deploying and using AI for education is recommended to address the issue.

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Section: Theoretical Discussionmentioning

confidence: 99%

Impact of artificial intelligence on human loss in decision making, laziness and safety in education

Ahmad,

Han,

Alam

et al. 2023

Humanit Soc Sci Commun

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“…Betweenness measures how often a given node connects a network's most critical paths; thus 𝐵𝐶 𝑖 is the importance of that given node in those networks [63]. This helps to identify the key components and the robustness of networks subject to disruptions in specific localities.…”

Section: B Weighted Betweenness Centralitymentioning

confidence: 99%

Benchmark Analysis for Robustness of Multi-Scale Urban Road Networks Under Global Disruptions

Shang

Gao

Daina

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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To date immunity to disruptions of multi-scale urban road networks (URNs) has not been effectively quantified. This study uses robustness as a meaningful -if partialrepresentation of immunity. We propose a novel Relative Area Index (RAI) based on traffic assignment theory to quantitatively measure the robustness of URNs under global capacity degradation due to three different types of disruptions, which takes into account many realistic characteristics. We also compare the RAI with weighted betweenness centrality, a traditional topological metric of robustness. We employ six realistic URNs as case studies for this comparison. Our analysis shows that RAI is a more effective measure of the robustness of URNs when multiscale URNs suffer from global disruptions. This improved effectiveness is achieved because of RAI's ability to capture the effects of realistic network characteristics such as network topology, flow patterns, link capacity, and travel demand. Also, the results highlight the importance of central management when URNs suffer from disruptions. Our novel method may provide a benchmark tool for comparing robustness of multi-scale URNs, which facilitates the understanding and improvement of network robustness for the planning and management of URNs.

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“…The "space of place" has been replaced by "space of flow," and the urban networks have gradually become a new perspective for studying urban systems [11,12]. Scholars have explored the structural characteristics, evolutionary trends, and influencing factors of urban networks, including enterprise networks [13][14][15], airline networks [16,17], logistics networks [18], freight networks [19], and information networks [20][21][22]. Presently, the increasingly complex social environment, urban connectivity, and diverse economic structures have made it essential to enhance the capability of urban networks to cope with shocks for maintaining sustainable regional development [23].…”

Section: Introductionmentioning

confidence: 99%

Research on the Evaluation of Resilience and Influencing Factors of the Urban Network Structure in the Three Provinces of Northeast China Based on Multiple Flows

Liu

Tian

et al. 2022

Buildings

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An important indicator for measuring the resilience and ability of urban networks to recover under external environmental shock, which is essential for the healthy development of the region, is urban network structure resilience. Herein we analyzed the resilience of the urban network structure and explored the influencing factors of resilience in the three provinces of Northeast China. We accomplished this by utilizing the Gephi profiling social network analysis tools based on the Baidu Index, road mileage, statistical data, other multi-source data, construction information, and the transportation, innovation, and economic multiple linkage network. This analysis enabled us to propose relevant suggestions and strategies to optimize urban network structure resilience. Our results indicate that (1) in 2019, the multi-city network structure in the three provinces of Northeast China contains both commonalities and characteristics. Overall, each network demonstrates a spatial distribution pattern of “dense in the north and sparse in the south.” (2) There exist evident hierarchical differences in the resilience characteristics of the multi-city network structure in the three provinces; each provincial capital city and sub-provincial city possesses greater advantages, the innovation network exhibits the most evident hierarchy, the mismatch of the information network is the highest, and the transmission and agglomeration of the economic network are the most prominent. (3) The resilience of the urban network structure of the three provinces is the result of the interaction of several factors. Political and economic factors such as government capacity, economic status, and urban vitality are the main factors affecting the resilience of the network structure.

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Identifying and mitigating security risks for secure and robust NGI networks

Abstract: The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription.

Cited by 10 publications

References 45 publications

Impact of artificial intelligence on human loss in decision making, laziness and safety in education

Impact of artificial intelligence on human loss in decision making, laziness and safety in education

Benchmark Analysis for Robustness of Multi-Scale Urban Road Networks Under Global Disruptions

Research on the Evaluation of Resilience and Influencing Factors of the Urban Network Structure in the Three Provinces of Northeast China Based on Multiple Flows

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