An Association Rule Mining-Based Framework for the Discovery of Anomalous Behavioral Patterns

Mehr, Azadeh Sadat Mozafari; Carvalho, Renata Medeiros de

doi:10.1007/978-3-031-22064-7_29

Cited by 1 publication

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“…5. Loan management process model [10] Table 1. Such data model is created according to domain knowledge and also indicates whether the user is allowed to repeat the execution of the data operations.…”

Section: Discussionmentioning

confidence: 99%

“…Repetition: is allowed (True), is not allowed (False). A: Application, O: Offer, W: Workflow [10]. can only be executed by a manager.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Table 2 shows mandatory data operation "Update(OCancelledFlag)" was ignored 40,869 times. We have also developed a view that provides detailed information, described in [10], which finds that this anomaly happened 16,735 times in the context of activity "W-Validate application ate abort", 16,184 times in the context of activity "W-Call after offers ate abort", and 7,950 times in the context of activity "A-Cancelled". Furthermore, it could detect who (in terms of roles and users) had the anomalous or suspicious behaviors during process executions.…”

Section: Discussionmentioning

confidence: 99%

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Detecting Complex Anomalous Behaviors in Business Processes: A Multi-perspective Conformance Checking Approach

Mehr

Carvalho

2023

Lecture Notes in Business Information Processing

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In recent years, organizations are putting an increasing emphasis on anomaly detection. Anomalies in business processes can be an indicator of system faults, inefficiencies, or even fraudulent activities. In this paper we introduce an approach for anomaly detection. Our approach considers different perspectives of a business process such as control flow, data and privacy aspects simultaneously.Therefore, it is able to detect complex anomalies in business processes like spurious data processing and misusage of authorizations. The approach has been implemented in the open source ProM framework and its applicability was evaluated through a real-life dataset from a financial organization. The experiment implies that in addition to detecting anomalies of each aspect, our approach can detect more complex anomalies which relate to multiple perspectives of a business process.

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Section: Discussionmentioning

confidence: 99%

“…Repetition: is allowed (True), is not allowed (False). A: Application, O: Offer, W: Workflow [10]. can only be executed by a manager.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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