Visualisation Tool to Support Fraud Detection

Maçãs, Catarina; Polisciuc, Evgheni; Machado, Penousal

doi:10.1109/iv53921.2021.00022

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…What these other data sources provided were simply the records and observations of attack sources and destinations of cyber incidents that occurred over the years. Moreover, there are other researches such as [23][24] [25] that also used data from data.world in their research. The main reason that differentiates it from other sources is the fact that it offers the ability to join and match different datasets, and also the ability for users to collectively share their data, which would make them more complete [25].…”

Section: Methodsmentioning

confidence: 99%

The Evolution of Cyberattack Motives

Boonkrong¹,

Prompunjai²,

Watcharawongbodee³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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A cyberattack can be defined as an action aiming to cause damages and losses to computer networks, information systems, and even personal devices and data. Many professionals and organizations have put a lot of effort and resources into preventing cyberattacks based on how they occur, their targets, and what damages they can cause. However, one of the aspects that are often overlooked and one of the reasons that cyberattacks are successfully carried out is the fact that the nature of attackers' motivations is not fully understood. Therefore, this research examines the main reasons for cyberattacks to be carried out by adversaries and the motives behind cyberattacks. Specifically, we studied over 7,700 cyber records and events between 2006 and 2018, including data breaches, privacy violations, and cyber incidents, to learn how attack motives have evolved over the years. The analyses of the data were mainly carried out using descriptive analysis. Our study found that the early cyberattacks were mainly financially motivated. However, in the later years, the cyberattack motives included espionage, ideology, and skill and knowledge testing. This implies that the motives behind cyberattacks became more varied in terms of types, proportions, and correlations between them. It is hoped and expected that the results of the analyses will be helpful to various stakeholders in such a way that they will better understand the reasons and motivations for cyberattacks.

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Section: Methodsmentioning

confidence: 99%

The Evolution of Cyberattack Motives

Boonkrong¹,

Prompunjai²,

Watcharawongbodee³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

show abstract

“…Dramatic changes are also an important point cut of fraudulent behaviors. Previous studies have designed multiple representation techniques to visualize temporal information, such as sequence visualization [19], [20], radial layouts [21], [22], and calendar [23], etc. FluxFlow [20] demonstrates the impact of anomalous information (e.g., rumors) spreading through colored circles packed on a timeline.…”

Section: Visual Analytics Approaches For Fraud Detectionmentioning

confidence: 99%

FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance

Zhou¹,

Wang²,

Wang³

et al. 2023

Preprint

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Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

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FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance

Zhou

Wang

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Visualisation Tool to Support Fraud Detection

Cited by 4 publications

References 31 publications

The Evolution of Cyberattack Motives

The Evolution of Cyberattack Motives

FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance

FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance

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