Transfer and online learning for IP maliciousness prediction in a concept drift scenario.
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| Title: | Transfer and online learning for IP maliciousness prediction in a concept drift scenario. |
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| Authors: | Escudero García, David1 (AUTHOR) descg@unileon.es, DeCastro-García, Noemí2 (AUTHOR) |
| Source: | Wireless Networks (10220038). Dec2024, Vol. 30 Issue 9, p7423-7444. 22p. |
| Subjects: | Data distribution, Internet protocol address, Online education, Transfer of training, Machine tools |
| Abstract: | Determining the maliciousness of a cybersecurity incident is essential to establish effective measures against it. To process large volumes of data in an automated way, machine learning techniques are commonly applied to the problem. One of the main obstacles to apply machine learning effectively is that the data distribution is not stationary, so a model trained on old data tends to degrade as new data with a different distribution is processed. This change in the distribution of data over time is known as concept drift and affects the reports of new events, which may compromise model performance. To tackle this problem this paper evaluates the effectiveness of transfer learning techniques in reducing the impact of concept drift on the performance of models for assigning maliciousness to IPs. We compare this approach with the application of online-updated models, which are another common approach to adapt to concept drift in the data. We analyse the performance of both approaches to determine which may be more effective in this setting. [ABSTRACT FROM AUTHOR] |
| Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 181064213 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Transfer and online learning for IP maliciousness prediction in a concept drift scenario. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Escudero+García%2C+David%22">Escudero García, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> descg@unileon.es</i><br /><searchLink fieldCode="AR" term="%22DeCastro-García%2C+Noemí%22">DeCastro-García, Noemí</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wireless+Networks+%2810220038%29%22">Wireless Networks (10220038)</searchLink>. Dec2024, Vol. 30 Issue 9, p7423-7444. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+distribution%22">Data distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+protocol+address%22">Internet protocol address</searchLink><br /><searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br /><searchLink fieldCode="DE" term="%22Transfer+of+training%22">Transfer of training</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+tools%22">Machine tools</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Determining the maliciousness of a cybersecurity incident is essential to establish effective measures against it. To process large volumes of data in an automated way, machine learning techniques are commonly applied to the problem. One of the main obstacles to apply machine learning effectively is that the data distribution is not stationary, so a model trained on old data tends to degrade as new data with a different distribution is processed. This change in the distribution of data over time is known as concept drift and affects the reports of new events, which may compromise model performance. To tackle this problem this paper evaluates the effectiveness of transfer learning techniques in reducing the impact of concept drift on the performance of models for assigning maliciousness to IPs. We compare this approach with the application of online-updated models, which are another common approach to adapt to concept drift in the data. We analyse the performance of both approaches to determine which may be more effective in this setting. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wireless Networks (10220038) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=181064213 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11276-024-03664-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 7423 Subjects: – SubjectFull: Data distribution Type: general – SubjectFull: Internet protocol address Type: general – SubjectFull: Online education Type: general – SubjectFull: Transfer of training Type: general – SubjectFull: Machine tools Type: general Titles: – TitleFull: Transfer and online learning for IP maliciousness prediction in a concept drift scenario. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Escudero García, David – PersonEntity: Name: NameFull: DeCastro-García, Noemí IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10220038 Numbering: – Type: volume Value: 30 – Type: issue Value: 9 Titles: – TitleFull: Wireless Networks (10220038) Type: main |
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