Construction of network topology and geographical vulnerability for telecommunication network

Liu, Meili; Pan, Hao

doi:10.1016/j.comnet.2022.108764

Cited by 5 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The general steps are: (1) The historical data of the nodes are processed for machine learning. (2) Experts are asked to modify some probabilities that do not conform to the distribution to complete the maximum practical value of the Bayesian network. (3) In the process of improving the CPT value, three scholars were required to first revise the CPT value of the sub-node based on a literature review and professional knowledge, and then two anti-fraud police officers were asked to further revise the CPT values based on realworld experience.…”

Section: Bayesian Network Probability Generationmentioning

confidence: 99%

Section: Bayesian Network Probability Generationmentioning

confidence: 99%

“…With the rapid development of the information era, telecom fraud has expanded from the traditional communication industry to other channels that deliver fraudulent information through Over The Top (OTT) platforms such as QQ, WeChat, and microblogs, and use them as a medium to spread fraudulent information, which seriously damages people's lives and property safety [1]. In 2021 alone, a total of 394,000 cases of telecommunication network fraud were solved and 634,000 suspects were arrested nationwide, which represents a year-on-year increase of 28.5% and 76.6% [2]. Therefore, prompt action is needed to tackle the issue of telecom-fraud governance.…”

Section: Introductionmentioning

confidence: 99%

“…The current problems in telecom-fraud technology research and application include: (1) not answering cause-andeffect questions such as "what, if? "; (2) there exists no quantitative research on external dynamic influences, such as major public health emergencies, police crackdown campaigns, and important time points; (3) failure to quantitatively assess the risk value and realize the risk warning for potential victims; and (4) the research mostly takes limited data (fraudulent SMS, suspicious phone numbers, etc.) as the object to provide governance ideas, and fails to break the barriers between multiple data.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network

et al. 2023

Entropy

View full text Add to dashboard Cite

In the construction of a telecom-fraud risk warning and intervention-effect prediction model, how to apply multivariate heterogeneous data to the front-end prevention and management of telecommunication network fraud has become one of the focuses of this research. The Bayesian network-based fraud risk warning and intervention model was designed by taking into account existing data accumulation, the related literature, and expert knowledge. The initial structure of the model was improved by utilizing City S as an application example, and a telecom-fraud analysis and warning framework was proposed by incorporating telecom-fraud mapping. After the evaluation in this paper, the model shows that age has a maximum sensitivity of 13.5% to telecom-fraud losses; anti-fraud propaganda can reduce the probability of losses above 300,000 yuan by 2%; and the overall telecom-fraud losses show that more occur in the summer and less occur in the autumn, and that the Double 11 period and other special time points are prominent. The model in this paper has good application value in the real-world field, and the analysis of the early warning framework can provide decision support for the police and the community to identify the groups, locations, and spatial and temporal environments prone to fraud, to combat propaganda and provide a timely warning to stop losses.

show abstract

Section: Bayesian Network Probability Generationmentioning

confidence: 99%

Section: Bayesian Network Probability Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…Complex network detection has been a popular topic in recent years because it involves a wide and deep range, including but not limited to telecommunications networks [16], community networks [17], traffic networks [18], and biological networks [19]. Among them, the nodes of the community networks are usually composed of people, and the connection between the nodes is dominated by familiarity, emotion, information, and other factors.…”

Section: Community Network Detection and Choquet Integralsmentioning

confidence: 99%

New Classifier Ensemble and Fuzzy Community Detection Methods Using POP Choquet-like Integrals

Zhang,

Jiang,

Wang

2023

Fractal Fract

View full text Add to dashboard Cite

Among various data analysis methods, classifier ensemble (data classification) and community network detection (data clustering) have aroused the interest of many scholars. The maximum operator, as the fusion function, was always used to fuse the results of the base algorithms in the classifier ensemble and the membership degree of nodes to classes in the fuzzy community. It is vital to use generalized fusion functions in ensemble and community applications. Since the Pseudo overlap function and the Choquet-like integrals are two new fusion functions, they can be combined as a more generalized fusion function. Along this line, this paper presents new classifier ensemble and fuzzy community detection methods using a pseudo overlap pair (POP) Choquet-like integral (expressed as a fraction). First, the pseudo overlap function pair is proposed to replace the product operator of the Choquet integral. Then, the POP Choquet-like integrals are defined to perform the combinatorial step of ensembles of classifiers and to generalize the GN modularity for the fuzzy community network. Finally, two new algorithms are designed for experiments, and some computational experiments with other algorithms show the importance of POP Choquet-like integrals. All of the experimental results show that our algorithms are practical.

show abstract

Optimizing communication network geodiversity for disaster resilience through shielding approach

Liu¹,

Pan²

2022

Reliability Engineering & System Safety

View full text Add to dashboard Cite

Construction of network topology and geographical vulnerability for telecommunication network

Cited by 5 publications

References 21 publications

A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network

A Framework for Analyzing Fraud Risk Warning and Interference Effects by Fusing Multivariate Heterogeneous Data: A Bayesian Belief Network

New Classifier Ensemble and Fuzzy Community Detection Methods Using POP Choquet-like Integrals

Optimizing communication network geodiversity for disaster resilience through shielding approach

Contact Info

Product

Resources

About