Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/105923
DC FieldValueLanguage
dc.contributor.authorHenriques, João-
dc.contributor.authorCaldeira, Filipe-
dc.contributor.authorCruz, Tiago-
dc.contributor.authorSimões, Paulo-
dc.date.accessioned2023-03-15T09:19:42Z-
dc.date.available2023-03-15T09:19:42Z-
dc.date.issued2020-
dc.identifier.issn2079-9292pt
dc.identifier.urihttps://hdl.handle.net/10316/105923-
dc.description.abstractComputing 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
dc.language.isoengpt
dc.publisherMDPIpt
dc.relationUIDB/05583/2020pt
dc.relationCISUC UID/CEC/00326/2020pt
dc.relationResearch Center in Digital Services (CISeD)pt
dc.relationPolytechnic of Viseupt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectanomaly detectionpt
dc.subjectclusteringpt
dc.subjectk-meanspt
dc.subjectgradient tree boostingpt
dc.subjectXGBoostpt
dc.titleCombining K-Means and XGBoost Models for Anomaly Detection Using Log Datasetspt
dc.typearticle-
degois.publication.firstPage1164pt
degois.publication.issue7pt
degois.publication.titleElectronics (Switzerland)pt
dc.peerreviewedyespt
dc.identifier.doi10.3390/electronics9071164pt
degois.publication.volume9pt
dc.date.embargo2020-01-01*
uc.date.periodoEmbargo0pt
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypearticle-
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.cerifentitytypePublications-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0001-7380-9511-
crisitem.author.orcid0000-0001-9278-6503-
crisitem.author.orcid0000-0002-5079-8327-
Appears in Collections:FCTUC Eng.Informática - Artigos em Revistas Internacionais
Show simple item record

Google ScholarTM

Check

Altmetric

Altmetric


This item is licensed under a Creative Commons License Creative Commons