AnomalyAdapters: Parameter-Efficient Multi-Anomaly Task Detection

dc.authoridUnal, Ugur/0000-0001-6552-6044
dc.authoridDAG, HASAN/0000-0001-6252-1870
dc.authorwosidDAG, HASAN/T-5301-2018
dc.contributor.authorDağ, Hasan
dc.contributor.authorDag, Hasan
dc.date.accessioned2023-10-19T15:11:53Z
dc.date.available2023-10-19T15:11:53Z
dc.date.issued2022
dc.department-temp[Unal, Ugur; Dag, Hasan] Kadir Has Univ, Management Informat Syst, TR-34083 Istanbul, Turkeyen_US
dc.description.abstractThe emergence of technological innovations brings sophisticated threats. Cyberattacks are increasing day by day aligned with these innovations and entails rapid solutions for defense mechanisms. These attacks may hinder enterprise operations or more importantly, interrupt critical infrastructure systems, that are essential to safety, security, and well-being of a society. Anomaly detection, as a protection step, is significant for ensuring a system security. Logs, which are accepted sources universally, are utilized in system health monitoring and intrusion detection systems. Recent developments in Natural Language Processing (NLP) studies show that contextual information decreases false-positives yield in detecting anomalous behaviors. Transformers and their adaptations to various language understanding tasks exemplify the enhanced ability to extract this information. Deep network based anomaly detection solutions use generally feature-based transfer learning methods. This type of learning presents a new set of weights for each log type. It is unfeasible and a redundant way considering various log sources. Also, a vague representation of model decisions prevents learning from threat data and improving model capability. In this paper, we propose AnomalyAdapters (AAs) which is an extensible multi-anomaly task detection model. It uses pretrained transformers' variant to encode a log sequences and utilizes adapters to learn a log structure and anomaly types. Adapter-based approach collects contextual information, eliminates information loss in learning, and learns anomaly detection tasks from different log sources without overuse of parameters. Lastly, our work elucidates the decision making process of the proposed model on different log datasets to emphasize extraction of threat data via explainability experiments.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [120E487]en_US
dc.description.sponsorshipThis work was supported in part by The Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 120E487.en_US
dc.identifier.citation4
dc.identifier.doi10.1109/ACCESS.2022.3141161en_US
dc.identifier.endpage5646en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85122849406en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage5635en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2022.3141161
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5266
dc.identifier.volume10en_US
dc.identifier.wosWOS:000744487400001en_US
dc.identifier.wosqualityQ2
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTask analysisen_US
dc.subjectAnomaly detectionen_US
dc.subjectAdaptation modelsen_US
dc.subjectTransformersen_US
dc.subjectSecurityen_US
dc.subjectSemanticsen_US
dc.subjectMonitoringen_US
dc.subjectAnomaly detectionen_US
dc.subjectadaptersen_US
dc.subjectcyber threat intelligenceen_US
dc.subjectexplainabilityen_US
dc.subjectlogen_US
dc.subjecttransfer learningen_US
dc.titleAnomalyAdapters: Parameter-Efficient Multi-Anomaly Task Detectionen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublicatione02bc683-b72e-4da4-a5db-ddebeb21e8e7
relation.isAuthorOfPublication.latestForDiscoverye02bc683-b72e-4da4-a5db-ddebeb21e8e7

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