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Combining K-Means and XGBoost Models for Anomaly Detection Using Log Datasets

dc.contributor.authorHenriques, João
dc.contributor.authorCaldeira, Filipe
dc.contributor.authorCruz, Tiago
dc.contributor.authorSimões, Paulo
dc.date.accessioned2022-11-18T11:34:36Z
dc.date.available2022-11-18T11:34:36Z
dc.date.issued2020-07-17
dc.date.updated2022-11-15T18:48:22Z
dc.description.abstractAbstract: Computing and networking systems traditionally record their activity in log files, which have been used for multiple purposes, such as troubleshooting, accounting, post-incident analysis of security breaches, capacity planning and anomaly detection. In earlier systems those log files were processed manually by system administrators, or with the support of basic applications for filtering, compiling and pre-processing the logs for specific purposes. However, as the volume of these log files continues to grow (more logs per system, more systems per domain), it is becoming increasingly difficult to process those logs using traditional tools, especially for less straightforward purposes such as anomaly detection. On the other hand, as systems continue to become more complex, the potential of using large datasets built of logs from heterogeneous sources for detecting anomalies without prior domain knowledge becomes higher. Anomaly detection tools for such scenarios face two challenges. First, devising appropriate data analysis solutions for effectively detecting anomalies from large data sources, possibly without prior domain knowledge. Second, adopting data processing platforms able to cope with the large datasets and complex data analysis algorithms required for such purposes. In this paper we address those challenges by proposing an integrated scalable framework that aims at efficiently detecting anomalous events on large amounts of unlabeled data logs. Detection is supported by clustering and classification methods that take advantage of parallel computing environments. We validate our approach using the the well known NASA Hypertext Transfer Protocol (HTTP) logs datasets. Fourteen features were extracted in order to train a k-means model for separating anomalous and normal events in highly coherent clusters. A second model, making use of the XGBoost system implementing a gradient tree boosting algorithm, uses the previous binary clustered data for producing a set of simple interpretable rules. These rules represent the rationale for generalizing its application over a massive number of unseen events in a distributed computing environment. The classified anomaly events produced by our framework can be used, for instance, as candidates for further forensic and compliance auditing analysis in security management.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationHenriques J, Caldeira F, Cruz T, Simões P. Combining K-Means and XGBoost Models for Anomaly Detection Using Log Datasets. Electronics. 2020; 9(7):1164. https://doi.org/10.3390/electronics9071164
dc.identifier.doi10.3390/electronics9071164pt_PT
dc.identifier.slugcv-prod-1995214
dc.identifier.urihttp://hdl.handle.net/10400.19/7410
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectanomaly detectionpt_PT
dc.subjectclusteringpt_PT
dc.subjectk-meanspt_PT
dc.subjectgradient tree boostingpt_PT
dc.subjectXGBoostpt_PT
dc.titleCombining K-Means and XGBoost Models for Anomaly Detection Using Log Datasetspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1164pt_PT
oaire.citation.issue7pt_PT
oaire.citation.startPage1164pt_PT
oaire.citation.titleElectronicspt_PT
oaire.citation.volume9pt_PT
person.familyNameMenoita Henriques
person.familyNameCaldeira
person.givenNameJoão Pedro
person.givenNameFilipe
person.identifierhttps://scholar.google.pt/citations?user=AExQrJwAAAAJ
person.identifierlXPmBvYAAAAJ
person.identifier.ciencia-idBB15-BFE2-17AA
person.identifier.ciencia-idCB11-8109-AB1D
person.identifier.orcid0000-0001-7380-9511
person.identifier.orcid0000-0001-7558-2330
person.identifier.scopus-author-id36023210300
rcaap.cv.cienciaidCB11-8109-AB1D | Filipe Caldeira
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication9b3258cd-a3d1-46f9-bc04-2bdd99d87014
relation.isAuthorOfPublicatione845705e-5b0b-4f70-9c53-c472ffd768d1
relation.isAuthorOfPublication.latestForDiscoverye845705e-5b0b-4f70-9c53-c472ffd768d1

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