Runtime security monitoring by an interplay between rule matching and deep learning-based anomaly detection on logs

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dc.contributor.authorAntic, Jan
dc.contributor.authorCosta, Joao Pita
dc.contributor.authorCernivec, Ales
dc.contributor.authorCankar, Matija
dc.contributor.authorMartincic, Tomaz
dc.contributor.authorPotocnik, Aljaz
dc.contributor.authorRatkajec, Hrvoje
dc.contributor.authorElguezabal, Gorka Benguria
dc.contributor.authorLeligou, Nelly
dc.contributor.authorLakka, Alexandra
dc.contributor.authorBoigues, Ismael Torres
dc.contributor.authorMorte, Eliseo Villanueva
dc.contributor.institutionHPA
dc.date.accessioned2024-07-24T11:45:33Z
dc.date.available2024-07-24T11:45:33Z
dc.date.issued2023
dc.descriptionPublisher Copyright: © 2023 IEEE.
dc.description.abstractIn the era of digital transformation the increasing vulnerability of infrastructure and applications is often tied to the lack of technical capability and the improved intelligence of the attackers. In this paper, we discuss the complementarity between static security monitoring of rule matching and an application of self-supervised machine-learning to cybersecurity. Moreover, we analyse the context and challenges of supply chain resilience and smart logistics. Furthermore, we put this interplay between the two complementary methods in the context of a self-learning and self-healing approach.en
dc.description.sponsorshipACKNOWLEDGMENT This project has received funding from the European Union’s Horizon 2020 research and innovation programmes under Grant Agreements No. 101000162 (PIACERE), 952644 (FISHY) and MEDINA (952633) This project has received funding from the European Union s Horizon 2020 research and innovation programmes under Grant Agreements No. 101000162 (PIACERE), 952644 (FISHY) and MEDINA (952633)
dc.description.statusPeer reviewed
dc.identifier.citationAntic , J , Costa , J P , Cernivec , A , Cankar , M , Martincic , T , Potocnik , A , Ratkajec , H , Elguezabal , G B , Leligou , N , Lakka , A , Boigues , I T & Morte , E V 2023 , Runtime security monitoring by an interplay between rule matching and deep learning-based anomaly detection on logs . in 2023 19th International Conference on the Design of Reliable Communication Networks, DRCN 2023 . 2023 19th International Conference on the Design of Reliable Communication Networks, DRCN 2023 , Institute of Electrical and Electronics Engineers Inc. , 19th International Conference on the Design of Reliable Communication Networks, DRCN 2023 , Vilanova i la Geltru , Spain , 17/04/23 . https://doi.org/10.1109/DRCN57075.2023.10108105
dc.identifier.citationconference
dc.identifier.doi10.1109/DRCN57075.2023.10108105
dc.identifier.isbn9781665475983
dc.identifier.urihttps://hdl.handle.net/11556/1472
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85159134927&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2023 19th International Conference on the Design of Reliable Communication Networks, DRCN 2023
dc.relation.ispartofseries2023 19th International Conference on the Design of Reliable Communication Networks, DRCN 2023
dc.relation.projectIDEuropean Union s Horizon 2020
dc.relation.projectIDHorizon 2020 Framework Programme, H2020, 952644-101000162-952633
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsanomaly detection
dc.subject.keywordsdeep learning
dc.subject.keywordsmasked language modelling
dc.subject.keywordsnatural language processing
dc.subject.keywordsruntime
dc.subject.keywordssecurity monitoring
dc.subject.keywordsself healing
dc.subject.keywordsself learning
dc.subject.keywordssmart logistics
dc.subject.keywordssupply chain resilience
dc.subject.keywordsHardware and Architecture
dc.subject.keywordsSafety, Risk, Reliability and Quality
dc.subject.keywordsComputer Networks and Communications
dc.subject.keywordsSDG 9 - Industry, Innovation, and Infrastructure
dc.titleRuntime security monitoring by an interplay between rule matching and deep learning-based anomaly detection on logsen
dc.typeconference output
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