An Agent-Based Modeling and Evolutionary Optimization Approach for Vulnerability Analysis of Critical Infrastructure Networks

Kizhakkedath, A.; Tai, Kang; Sim, Mong Soon; Tiong, Robert L. K.; Lin, Jiaying

doi:10.1007/978-3-642-45037-2_17

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…The clustering algorithm is that of grouping finite arcos as valid transaction. In this manner, the results are used to analyze the proposed anti-money laundering after fraudulent properties of data processing technologies [15] [16].There are no rules on regular money laundering and money laundering as we choose a suitable strategy to track down. Therefore, with the central decision tree algorithm, we can more efficiently identify extraordinary transactions data [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying Suspicious Money Laundering Transaction Based on Collaborative Relational Data Screening Model Using Decision Classifier in Transactional Database

2020

jcr

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Transactional data be most economical concern in banking sectors for financial prospects. Due to criminal activities and tolerant they had many issues specifically in money laundering. The illegal transaction, robbery, malpractice non related source of access, are tedious to identifying legal transactions. By the higher transactions using the big data analysis to identify possible money laundering activities. To resolve the issue to make effective future selection approach in transactions and classify the resultant under data cleaning, partial information from traditional statistical analysis, and data mining process. To propose an collaborative relational data screening model (CRDSCM) using decision classifier in transactional database. Autocorrelation function with screening cluster approach were conducted to examine the relationship between the attribute similarities on each transaction collaborative data analysis method. With support of time serious data including predictive analytics for decision making the detection of attitude and money laundering activities that are used to deliver classified result in efficient way

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Section: Literature Reviewmentioning

confidence: 99%

Identifying Suspicious Money Laundering Transaction Based on Collaborative Relational Data Screening Model Using Decision Classifier in Transactional Database

2020

jcr

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“…Ouyang [8] carried out an in-depth review of modeling and simulation approaches used to assess the vulnerability of critical infrastructure systems. These approaches include (see also [9,10]): Computational and optimization approaches, i.e., object-oriented programming, and agent-based modeling; simulation approaches, such as system dynamics-based modeling; network-based approaches; economic based approaches; and other approaches (for instance, approaches based on the implementation of petri-nets, dynamic control system theory, Bayesian networks). In the stream of the computational approach, Behrooz et al [11] used an optimization model to deal with the problem of demand uncertainty in the daily planning of natural gas transmission.…”

Section: Literaturementioning

confidence: 99%

An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure

Storto

2019

Energies

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This paper presents a framework to identify critical nodes of a gas pipeline network. This framework calculates a set of metrics typical of the social network analysis considering the topological characteristics of the network. Such metrics are utilized as inputs and outputs of a (Data Envelopment Analysis) DEA model to generate a cross-efficiency index that identifies the most important nodes in the network. The framework was implemented to assess the US interstate gas network between 2013 and 2017 from both the demand and supply-side perspectives. Results emerging from the US gas network case suggest that different analysis perspectives should necessarily be considered to have a more in-depth and comprehensive view of the network capacity and performance.

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“…The elementary critical infrastructure network constructed in QGIS contains the geographical location of the nodes and links, which provides the possibility of extension to geographical area damage simulation in future work. The failure scenarios within the physical infrastructure network, comprising of one-node failures, two-node failures and three-node failures, are simulated by assuming different combinations of failed nodes and computing the resulting impact on all critical infrastructure sectors interconnected in the I/O model (Kizhakkedath et al, 2013;Lam et al, 2013;Tai et al, 2013). Figure 4 shows the graphical representation of the Netlogo program for 'one physical network' consisting of the telecommunication network and 'two physical networks' consisting of the telecommunication network and the electrical grid.…”

Section: Combining I/o Model and Physical Critical Infrastructure Netmentioning

confidence: 99%

Modelling critical infrastructure network interdependencies and failure

Lin

Tai

Kong

et al. 2019

IJCIS

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Critical infrastructures are fundamental systems that are interconnected with one another, and their interdependencies need to be modelled in order to analyse any vulnerabilities that exist in these infrastructure networks. In this work, a Leontief input-output model of the critical infrastructure systems and two physical critical infrastructure networks are developed. The input-output model serves to evaluate the interdependencies among the various infrastructure systems, while the physical infrastructure network describes the linkages within a single physical infrastructure system. This study examines the overall impact of any failures in the telecommunication infrastructure or electricity grid within the input-output interdependencies model systematically so as to evaluate the worst case scenarios based on the impact caused by one or more points of failure in the physical infrastructure.

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An Agent-Based Modeling and Evolutionary Optimization Approach for Vulnerability Analysis of Critical Infrastructure Networks

Cited by 12 publications

References 18 publications

Identifying Suspicious Money Laundering Transaction Based on Collaborative Relational Data Screening Model Using Decision Classifier in Transactional Database

Identifying Suspicious Money Laundering Transaction Based on Collaborative Relational Data Screening Model Using Decision Classifier in Transactional Database

An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure

Modelling critical infrastructure network interdependencies and failure

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