Visual Analytics of Anomalous User Behaviors: A Survey

Shi, Yang; Liu, Yuyin; Tong, Hanghang; He, Jingrui; Yan, Gang; Cao, Nan

doi:10.1109/tbdata.2020.2964169

Cited by 17 publications

(9 citation statements)

References 134 publications

(378 reference statements)

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“…The works of [13,33,64,65,66] were selected based on these criteria. Given the intersection with visual analytics literature for decision-making in fraud, we also adopt terminology from this domain for our systematic review based on [21,43] and [67][68][69][70][71][72]. Next, core and most cited surveys This is an open access post-print version; the final authenticated version is available online at https://link.springer.com/chapter/10.1007/978-3-030-57321-8_18 by © IFIP International Federation for Information Processing 2020.…”

Section: Validation Of Fraud Scenariosmentioning

confidence: 99%

“…This is an open access post-print version; the final authenticated version is available online at https://link.springer.com/chapter/10.1007/978-3-030-57321-8_18 by © IFIP International Federation for Information Processing 2020. [22,36,43,44,55,68,71,72,80,88,89,92,93,95,96,97,98,101,102,103,104,105,106,107] 24 Yes…”

Section: Usage Of Scenarios For Requirements Elicitation For Explanatmentioning

confidence: 99%

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Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

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Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder's requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenariobased design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection.

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Section: Validation Of Fraud Scenariosmentioning

confidence: 99%

Section: Usage Of Scenarios For Requirements Elicitation For Explanatmentioning

confidence: 99%

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

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“…Yang et al [22] presented a comprehensive survey of visual analytics of anomalous user behaviors and classified them into four categories: social interaction, travel, network communication, and transaction. They also categorized visualization techniques that have been applied to anomalous user behaviors, including, among others: sequence (illustrates the relations between successive events with temporal information); graph (shows structured patterns composed of nodes and edges); chart (represents the attributes of a multidimensional data item using a chart).…”

Section: Visual Analytics and Fraud Auditmentioning

confidence: 99%

Fraud Audit Based on Visual Analysis: A Process Mining Approach

et al. 2021

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Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows making inferences about a population. Mistakes are usually committed when generalizing the results and anomalies; therefore, they appear in unprocessed sets; however, there are some efforts to address these limitations using process-mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patterns. This paper presents a fraud audit approach based on the combination of process mining techniques and visual analytics. The main advantages are: (i) a method is included that guides the use of the visual capabilities of process mining to detect fraud data patterns during an audit; (ii) the approach can be generalized to any business domain; (iii) well-known process mining techniques are used (dotted chart, trace alignment, fuzzy miner…). The techniques were selected by a group of experts and were extended to enable filtering for contextual analysis, to handle levels of process abstraction, and to facilitate implementation in the area of fraud audits. Based on the proposed approach, we developed a software solution that is currently being used in the financial sector as well as in the telecommunications and hospitality sectors. Finally, for demonstration purposes, we present a real hotel management use case in which we detected suspected fraud behaviors, thus validating the effectiveness of the approach.

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“…Concretely, the following research works analyse anomalous user behaviour, anomalous information spread and the use of toxic language. (Shi et al, 2019) carried out a survey on the visual analytics of anomalous user behaviours. The survey revealed four types of user behaviours, including social interaction, travel, network communication and transactions.…”

Section: Visualisations For Monitoring Automatic Annotation Systemsmentioning

confidence: 99%

Data Visualization for Supporting Linguists in the Analysis of Toxic Messages

Kavaz¹,

Puig²,

Rodríguez³

et al. 2021

CSRN

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The goal of this research is to provide linguists with visualisations for analysing the results of their hate speech annotation. These visualisations consist of a set of interactive graphs for analysing the global distribution of annotated messages, finding relationships between features, and detecting inconsistencies in the annotation. We used a corpus that includes 1,262 comments posted in response to different Spanish online new articles. The comments were annotated with features such as sarcasm, mockery, insult, improper language, constructivity and argumentation, as well as with level of toxicity ('not-toxic', 'mildly toxic', 'toxic' or 'very toxic'). We evaluated the selected visualisations with users to assess the graphs' comprehensibility, interpretability and attractiveness. One of the lessons learned from the study is the usefulness of mixed visualisations that include simple graphs (Bar, Heat map) -to facilitate the familiarisation with the results of the annotated corpus together with more complex ones (Sankey, Spider or Chord) -to explore and identify relationships between features and to find inconsistencies.

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Visual Analytics of Anomalous User Behaviors: A Survey

Cited by 17 publications

References 134 publications

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Fraud Audit Based on Visual Analysis: A Process Mining Approach

Data Visualization for Supporting Linguists in the Analysis of Toxic Messages

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