Ontology Based Anomaly Detection for File Integration

Can, Özgü; Uzum, Ibrahim

doi:10.1007/978-3-030-36599-8_17

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…In the literature, there is no machine learning-based anomaly detection approach that considers enterprise file integrations. Ontology-based approaches within the scope of anomalous file integration are presented in Can and Uzum, 2019). In addition, problems that occur within the scope of information security in file integrations are presented in (Uzum and Can, 2018a;Uzum and Can, 2018b).…”

Section: Related Workmentioning

confidence: 99%

Büyükbaş Hayvancılık Yetiştiriciliği Hakkında Genel Bilgi: Türkiyeden Örnekler

ALHAJ¹

2021

jocrest

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Information systems that are based on real-time file integrations have an essential role to improve the quality of organizations' business process management. File transfers and data integrations between discrete systems have gained great importance. However, network and security issues have emerged due to the integration of file transfer processes and the structure of files. Thus, an effective and self-learning anomaly detection approach is needed for the file integration processes to provide the persistence of integration channels, data integrity, and availability of file transfer processes. A novel anomaly detection approach that focuses on file transfers between discrete systems is proposed in this paper. The proposed anomaly detection approach is a self-learning module for the file transfer processes. For this purpose, anomalies that occur in file transfer processes are detected by applying machine learning techniques. Four clusteringbased machine learning approaches are applied to detect anomalies in the file integration processes: Elliptic Envelope, Isolation Forest, Local Outlier Factor, and One-Class Support Vector Machine.

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Section: Related Workmentioning

confidence: 99%

Büyükbaş Hayvancılık Yetiştiriciliği Hakkında Genel Bilgi: Türkiyeden Örnekler

ALHAJ¹

2021

jocrest

View full text Add to dashboard Cite

show abstract

Ontology Based Anomaly Detection for File Integration

Cited by 1 publication

References 9 publications

Büyükbaş Hayvancılık Yetiştiriciliği Hakkında Genel Bilgi: Türkiyeden Örnekler

Büyükbaş Hayvancılık Yetiştiriciliği Hakkında Genel Bilgi: Türkiyeden Örnekler

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