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Evaluating large language models effectiveness for flow-based intrusion detection: a comparative study with ML and DL baselines.

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Title: Evaluating large language models effectiveness for flow-based intrusion detection: a comparative study with ML and DL baselines.
Authors: Mehavilla, Lorena1, lmehavilla@unizar.es, Rodríguez, María1, maria.rodriguezg@unizar.es, García, José1, jogarmo@unizar.es, Alesanco, Álvaro1, alesanco@unizar.es
Source: Artificial Intelligence Review; Feb2026, Vol. 59 Issue 2, p1-38, 38p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 190872070
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Evaluating large language models effectiveness for flow-based intrusion detection: a comparative study with ML and DL baselines.
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  Data: <searchLink fieldCode="AU" term="%22Mehavilla%2C+Lorena%22">Mehavilla, Lorena</searchLink><relatesTo>1</relatesTo>, <i>lmehavilla@unizar.es</i><br /><searchLink fieldCode="AU" term="%22Rodríguez%2C+María%22">Rodríguez, María</searchLink><relatesTo>1</relatesTo>, <i>maria.rodriguezg@unizar.es</i><br /><searchLink fieldCode="AU" term="%22García%2C+José%22">García, José</searchLink><relatesTo>1</relatesTo>, <i>jogarmo@unizar.es</i><br /><searchLink fieldCode="AU" term="%22Alesanco%2C+Álvaro%22">Alesanco, Álvaro</searchLink><relatesTo>1</relatesTo>, <i>alesanco@unizar.es</i>
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  Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+Review%22">Artificial Intelligence Review</searchLink>; Feb2026, Vol. 59 Issue 2, p1-38, 38p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=190872070
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10462-025-11432-2
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      – Code: eng
        Text: English
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        PageCount: 38
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      – TitleFull: Evaluating large language models effectiveness for flow-based intrusion detection: a comparative study with ML and DL baselines.
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            NameFull: Mehavilla, Lorena
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              Text: Feb2026
              Type: published
              Y: 2026
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              Value: 59
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