Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols.
Saved in:
| Title: | Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols. |
|---|---|
| Authors: | Al-Obeidat, F.1 (AUTHOR), El-Alfy, E.-S. M.2 (AUTHOR) alfy@kfupm.edu.sa |
| Source: | Personal & Ubiquitous Computing. Nov2019, Vol. 23 Issue 5/6, p777-791. 15p. 3 Diagrams, 17 Charts. |
| Subjects: | Traffic patterns, Computer security management, Traffic flow, Decision trees, Fuzzy decision making, Computer networks, Quality of service, Computer network protocols |
| Abstract: | Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of Personal & Ubiquitous Computing 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 139745187 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Al-Obeidat%2C+F%2E%22">Al-Obeidat, F.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22El-Alfy%2C+E%2E-S%2E+M%2E%22">El-Alfy, E.-S. M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> alfy@kfupm.edu.sa</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Personal+%26+Ubiquitous+Computing%22">Personal & Ubiquitous Computing</searchLink>. Nov2019, Vol. 23 Issue 5/6, p777-791. 15p. 3 Diagrams, 17 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Traffic+patterns%22">Traffic patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+security+management%22">Computer security management</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+flow%22">Traffic flow</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+decision+making%22">Fuzzy decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+networks%22">Computer networks</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+of+service%22">Quality of service</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+protocols%22">Computer network protocols</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Personal & Ubiquitous Computing 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=139745187 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00779-017-1096-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 777 Subjects: – SubjectFull: Traffic patterns Type: general – SubjectFull: Computer security management Type: general – SubjectFull: Traffic flow Type: general – SubjectFull: Decision trees Type: general – SubjectFull: Fuzzy decision making Type: general – SubjectFull: Computer networks Type: general – SubjectFull: Quality of service Type: general – SubjectFull: Computer network protocols Type: general Titles: – TitleFull: Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Al-Obeidat, F. – PersonEntity: Name: NameFull: El-Alfy, E.-S. M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 16174909 Numbering: – Type: volume Value: 23 – Type: issue Value: 5/6 Titles: – TitleFull: Personal & Ubiquitous Computing Type: main |
| ResultId | 1 |
