A Detection Method for Abnormal Transactions in E-Commerce Based on Extended Data Flow Conformance Checking

Wang, Yadi; Yu, Wangyang; Teng, Peng; Liu, Guanjun; Xiang, Dongming

doi:10.1155/2022/4434714

Cited by 9 publications

(7 citation statements)

References 39 publications

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“…Petri nets PN is a modeling and analysis tool for distributed systems. It is particularly convenient for describing the sequence, concurrency, conflict, and synchronization of system processes (Yu et al, 2018b;Wang et al, 2022). PN can provide the graphical notations and the basic primitives for modeling concurrency, communication, and synchronization.…”

Section: Modeling Schemementioning

confidence: 99%

Modeling and analyzing the action process of monoamine hormones in depression: a Petri nets-based intelligent approach

Wang,

Yu,

Zhang

et al. 2023

Front. Big Data

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In contemporary society, the incidence of depression is increasing significantly around the world. At present, most of the treatment methods for depression are psychological counseling and drug therapy. However, this approach does not allow patients to visualize the logic of hormones at the pathological level. In order to better apply intelligence computing methods to the medical field, and to more easily analyze the relationship between norepinephrine and dopamine in depression, it is necessary to build an interpretable graphical model to analyze this relationship which is of great significance to help discover new treatment ideas and potential drug targets. Petri net (PN) is a mathematical and graphic tool used to simulate and study complex system processes. This article utilizes PN to study the relationship between norepinephrine and dopamine in depression. We use PN to model the relationship between the norepinephrine and dopamine, and then use the invariant method of PN to verify and analyze it. The mathematical model proposed in this article can explain the complex pathogenesis of depression and visualize the process of intracellular hormone-induced state changes. Finally, the experiment result suggests that our method provides some possible research directions and approaches for the development of antidepressant drugs.

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Section: Modeling Schemementioning

confidence: 99%

Modeling and analyzing the action process of monoamine hormones in depression: a Petri nets-based intelligent approach

Wang,

Yu,

Zhang

et al. 2023

Front. Big Data

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“…Afterwards, they resell these commodities at higher prices in the second-hand market to gain huge illegal profits. In order to effectively q (1) p q (2) Z (2) Encoder View 2…”

Section: The Proposed Modelmentioning

confidence: 99%

“…To address the problem in the above fraudulent scene, it is particularly urgent to design effective antifraud strategies and technologies. As an important data mining technique, anomaly detection has been successfully applied in various fields [1,2]; it also reveals enormous potential in the e-commerce fraud recognition.…”

Section: Introductionmentioning

confidence: 99%

Abnormal User Detection via Multiview Graph Clustering in the Mobile e-Commerce Network

Wang

2022

Wireless Communications and Mobile Computing

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In recent years, Internet of Things has not only promoted the continuous development of e-commerce transaction but also brought loop-hole to the fraud gangs who always utilize mobile devices to commit fraud crimes. For example, fraud gangs are usually organized to purchase commodities at low prices in e-commerce promotions. They benefit from the price spread by reselling commodities at high prices. In the past few years, the transaction fraud caused serious financial losses to merchants in e-commerce platform. To detect the fraudulent user and behavior effectively, a multiview graph clustering-based abnormal detection model is developed in this paper. In the proposed model, two fraudulent behavior patterns are proposed by abstracting the e-commerce network as a heterogeneous information graph. On this basis, two user-similarity graphs are reorganized from the heterogeneous graph with the help of different metapaths. Subsequently, in order to capture the corresponding fraudulent behavior patterns, the above two graphs are encoded into user embeddings and assigned to specific clusters in respective views. Finally, the consensus detection result is produced by fusing the complementary information of different views in a joint multiview learning framework. As we know, our work is the first one that uses multiview graph clustering in e-commerce fraud detection, which will provide a new research perspective for fraud detection in e-commerce platform. Extensive experiments are conducted on real and semisynthetic datasets, and the results demonstrate the effectiveness and superiority of the proposed model.

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“…Even a minor logic vulnerability can lead to serious impacts in mission critical financial applications, thus logic vulnerabilities pose serious threats to the security of e-commerce applications [1], [4], [5]. In such a hybrid system, coordinating the involved participants in a secure fashion is highly challenging, logic flaws are pervasive in modern e-commerce systems [3], [6], [7], [8], [9], [10]. Application and business logic refers to application-specific functionality and behaviors in concurrent business interactions.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analyzing Logic Vulnerabilities of E-Commerce Systems at the Design Phase

Yu,

Liu,

Wang

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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E-commerce systems have become tremendously popular and important for modern business processes in the world of digital economy. E-commerce business processes rely on the distributed and concurrent interaction process among web applications of participants, such as clients, merchants, third-party payment platforms (TPPs) and bank systems. Such complex business interactions bridge the gap of trustiness among participants and introduce new security challenges in the form of logical vulnerabilities, which are prevalent in the business process at the application level. The most pressing challenge is to guarantee security throughout the checkout process at the conceptual design phase such that the logic errors can be detected before the actual implementation. Maintenance and repair of implemented e-commerce systems can be extremely costly. To this end, this paper proposes a novel modeling and analyzing methodology for multi-participants and multi-sessions e-commerce interaction processes based on Colored Petri nets (CPN). Firstly, we define a novel model that can efficiently depict the key properties of e-commerce business interaction processes. Secondly, several modeling principles are formulated based on the design specification of e-commerce systems. Finally, the concept of Transaction-Logical Consistency is defined to analyze and verify the logical vulnerabilities of e-commerce systems. Through a discussed case study, we demonstrate the feasibility and applicability of the proposed methodology and its efficiency in detecting problems those can potentially lead to logical vulnerabilities.

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A Detection Method for Abnormal Transactions in E-Commerce Based on Extended Data Flow Conformance Checking

Cited by 9 publications

References 39 publications

Modeling and analyzing the action process of monoamine hormones in depression: a Petri nets-based intelligent approach

Modeling and analyzing the action process of monoamine hormones in depression: a Petri nets-based intelligent approach

Abnormal User Detection via Multiview Graph Clustering in the Mobile e-Commerce Network

Modeling and Analyzing Logic Vulnerabilities of E-Commerce Systems at the Design Phase

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