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Privacy and Security in a B2B environment : Focus on Supplier Impersonation Fraud Detection using Data Analysis

Abstract : Supplier Impersonation Fraud (SIF) is a kind of fraud occuring in a Business-To-Business context (B2B), where a fraudster impersonates a supplier in order to trigger an illegitimate payment from a company. Most of the exisiting systems focus solely on a single, "intra-company" approach in order to detect such kind of fraud. However, the companies are part of an ecosystem where multiple agents interacts, and such interaction hav yet to be integrated as a part of the existing detection techniques. In this thesis we propose to use state-of-the-art techniques in Machine Learning in order to build a detection system for such frauds, based on the elaboration of a model using historical transactions from both the targeted companies and the relevant other companies in the ecosystem (contextual data). We perform detection of anomalous transactions when significant change in the payment behavior of a company is detected. Two ML-based systems are proposed in this work: ProbaSIF and GraphSIF. ProbaSIF uses a probabilistic approach (urn model) in order to asert the probability of occurrence of the account used in the transaction in order to assert its legitimacy. We use this approach to assert the differences yielded by the integration of contextual data to the analysis. GraphSIF uses a graph-based approach to model the interaction between client and supplier companies as graphs, and then uses these graph as training data in a Self-Organizing Map-Clustering model. The distance between a new transaction and the center of the cluster is used to detect changes in the behavior of a client company. These two systems are compared with a real-life fraud detection system in order to assert their performance.
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https://tel.archives-ouvertes.fr/tel-03125757
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Submitted on : Friday, January 29, 2021 - 4:51:07 PM
Last modification on : Saturday, January 30, 2021 - 3:29:44 AM

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  • HAL Id : tel-03125757, version 1

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Rémi Canillas. Privacy and Security in a B2B environment : Focus on Supplier Impersonation Fraud Detection using Data Analysis. Cryptography and Security [cs.CR]. Université de Lyon, 2020. English. ⟨NNT : 2020LYSEI118⟩. ⟨tel-03125757⟩

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