Community Detection for Mobile Money Fraud Detection

Ayeb, Safa El; Hemery, Baptiste; Jeanne, Fabrice; Cherrier, Estelle

doi:10.1109/snams52053.2020.9336578

Cited by 9 publications

(7 citation statements)

References 32 publications

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“…A node can simultaneously be part of multiple communities of different scopes and levels, such as family, friends, work, city, etc. [6]. Overlapping communities were studied in the literature in various contexts such as biology [7], e-commerce [8], mobile networks [2], etc.…”

Section: A Overlapping Communities Detectionmentioning

confidence: 99%

Evaluation Metrics for Overlapping Community Detection

Ayeb

Hemery

Jeanne

et al. 2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

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Networks have provided a representation for a wide range of real systems, including communication flow, money transfer or biological systems, to mention just a few. Communities represent fundamental structures for understanding the organization of real-world networks. Uncovering coherent groups in these networks is the goal of community detection. A community is a mesoscopic structure with nodes heavily connected within their groups by comparison to the nodes in other groups. Communities might also overlap as they may share one or multiple nodes. Evaluating the results of a community detection algorithm is an equally important task. This paper introduces metrics for evaluating overlapping community detection. The idea of introducing new metrics comes from the lack of efficiency and adequacy of state-of-the-art metrics for overlapping communities. The new metrics are tested both on simulated data and standard datasets and are compared with existing metrics.

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Section: A Overlapping Communities Detectionmentioning

confidence: 99%

Evaluation Metrics for Overlapping Community Detection

Ayeb

Hemery

Jeanne

et al. 2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

Self Cite

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“…For such a service, transactions take place between customers for various reasons such as sending money for family, transfers between friends, salary payment, etc. Social network analysis has already been successfully used on banking and money transfer data to carry out socio-economic studies [5], uncover customer buying habits [6], fight fraud [7], etc.…”

Section: Context a Use Case Around Financial Datamentioning

confidence: 99%

Multigraph transformation for community detection applied to financial services

Ayeb

Hemery

Jeanne

et al. 2022

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Copyright

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“…The fraudster may gather information about the victim through various means, such as social engineering [8] or hacking [9]. Once a victim has been identified, the fraudster typically initiates a contact with them through a variety of means, such as email [10], phone [11], or social media [12]. The next step involves the fraudster tricking the victim into providing sensitive information or performing an action that benefits the fraudster, such as transferring funds or installing malicious content [13].…”

Section: Introductionmentioning

confidence: 99%

Class imbalance aware drift identification model for detecting diverse attack in streaming environment

Shahapurkar,

Patil,

K. Tangod

2024

IJEECS

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<div align="center"><span>Detecting fraudulent transactions in a streaming environment presents several challenges including the large volume of data, the need for real-time detection, and the potential for data drift. To address these challenges a robust model is needed that utilizes machine learning techniques to classify transactions in real-time. Hence, this paper proposes a model for detecting fraudulent transactions in a streaming environment using xtream gradient boost (XGBoost), cross-validation and class imbalance aware drift identification (CIADI) model. The performance of the proposed method is evaluated using datasets named credit card and Network Security Laboratory (NSL-KDD) dataset. The results demonstrate that the model can effectively detect fraudulent transactions with high accuracy, recall, and F-measure. The results show that the proposed CIADI model attained 95.63% for the credit card dataset which is higher accuracy in comparison to the generative-adversarial networks (GAN), network-anomaly-detection scheme-based on feature-representation and data-augmentation (NADS-RA) and feature-aware XGBoost (FA-XGB). Further the proposed CIADI model attained 98.5% for the NSL-KDD dataset which is higher accuracy in comparison to the NADS-RA, stacked-nonsymmetric deep-autoencoder (sNDAE) and convolutional neural-network (CNN). This study suggests that the proposed method can be an effective model for detecting fraudulent transactions in streaming environments.</span></div>

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Community Detection for Mobile Money Fraud Detection

Cited by 9 publications

References 32 publications

Evaluation Metrics for Overlapping Community Detection

Evaluation Metrics for Overlapping Community Detection

Multigraph transformation for community detection applied to financial services

Class imbalance aware drift identification model for detecting diverse attack in streaming environment

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