A Novel Feature Selection Method Based on Trochoid Search Optimization with an S-Z-Shaped Composite X-Shaped Transfer Function for for DDoS Attack Detection.
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| Title: | A Novel Feature Selection Method Based on Trochoid Search Optimization with an S-Z-Shaped Composite X-Shaped Transfer Function for for DDoS Attack Detection. |
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| Authors: | Jin, Si-Yu1 jsy@stu.ustl.edu.cn, Xing, Cheng2 xingcheng0811@163.com, Wang, Jie-Sheng3 wjs@ustl.edu.cn, Xu, Zi-Rui1 xzr@stu.ustl.edu.cn, Si, Zhi-Guang1 szg@stu.ustl.edu.cn |
| Source: | Engineering Letters. Jul2026, Vol. 34 Issue 7, p2498-2515. 18p. |
| Subjects: | Feature selection, Denial of service attacks, Transfer functions, Optimization algorithms, Combinatorial optimization, Detection algorithms, Computer network traffic |
| Abstract: | Feature selection is a crucial step in classification tasks, capable of filtering out a subset of features that are more discriminative than the original features, thereby enhancing the model's generalization ability and reducing computational complexity. In DDoS attack detection, network traffic data typically exhibits characteristics of high dimensionality, strong redundancy and abundant noise, which directly impact detection efficiency and recognition accuracy. Therefore, efficient and stable feature selection is particularly important. Since the feature subset can be represented as a 0 and 1 vector, the feature selection problem is usually a binary combinatorial optimization problem. For this reason, a trochoid search optimization algorithm based on a composite X-shaped transfer function is proposed. This method, based on the optimization update mechanism of trochoid search, constructs a composite X-shaped transfer function that integrates the boundary suppression ability of the Z-shaped function and the smooth mapping characteristics of the S-shaped function, achieving a stable mapping from continuous solutions to binary feature subsets, thereby enhancing the balance between global exploration and local exploitation, reducing the risk of premature convergence, and improving the stability and repeatability of binary decisions. Through comparative experiments on 9 UCI datasets and 2 DDoS attack datasets, the effectiveness and applicability of this method have been verified. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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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| Items | – Name: Title Label: Title Group: Ti Data: A Novel Feature Selection Method Based on Trochoid Search Optimization with an S-Z-Shaped Composite X-Shaped Transfer Function for for DDoS Attack Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jin%2C+Si-Yu%22">Jin, Si-Yu</searchLink><relatesTo>1</relatesTo><i> jsy@stu.ustl.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xing%2C+Cheng%22">Xing, Cheng</searchLink><relatesTo>2</relatesTo><i> xingcheng0811@163.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jie-Sheng%22">Wang, Jie-Sheng</searchLink><relatesTo>3</relatesTo><i> wjs@ustl.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Zi-Rui%22">Xu, Zi-Rui</searchLink><relatesTo>1</relatesTo><i> xzr@stu.ustl.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Si%2C+Zhi-Guang%22">Si, Zhi-Guang</searchLink><relatesTo>1</relatesTo><i> szg@stu.ustl.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Jul2026, Vol. 34 Issue 7, p2498-2515. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Denial+of+service+attacks%22">Denial of service attacks</searchLink><br /><searchLink fieldCode="DE" term="%22Transfer+functions%22">Transfer functions</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+traffic%22">Computer network traffic</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Feature selection is a crucial step in classification tasks, capable of filtering out a subset of features that are more discriminative than the original features, thereby enhancing the model's generalization ability and reducing computational complexity. In DDoS attack detection, network traffic data typically exhibits characteristics of high dimensionality, strong redundancy and abundant noise, which directly impact detection efficiency and recognition accuracy. Therefore, efficient and stable feature selection is particularly important. Since the feature subset can be represented as a 0 and 1 vector, the feature selection problem is usually a binary combinatorial optimization problem. For this reason, a trochoid search optimization algorithm based on a composite X-shaped transfer function is proposed. This method, based on the optimization update mechanism of trochoid search, constructs a composite X-shaped transfer function that integrates the boundary suppression ability of the Z-shaped function and the smooth mapping characteristics of the S-shaped function, achieving a stable mapping from continuous solutions to binary feature subsets, thereby enhancing the balance between global exploration and local exploitation, reducing the risk of premature convergence, and improving the stability and repeatability of binary decisions. Through comparative experiments on 9 UCI datasets and 2 DDoS attack datasets, the effectiveness and applicability of this method have been verified. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 2498 Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Denial of service attacks Type: general – SubjectFull: Transfer functions Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Combinatorial optimization Type: general – SubjectFull: Detection algorithms Type: general – SubjectFull: Computer network traffic Type: general Titles: – TitleFull: A Novel Feature Selection Method Based on Trochoid Search Optimization with an S-Z-Shaped Composite X-Shaped Transfer Function for for DDoS Attack Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jin, Si-Yu – PersonEntity: Name: NameFull: Xing, Cheng – PersonEntity: Name: NameFull: Wang, Jie-Sheng – PersonEntity: Name: NameFull: Xu, Zi-Rui – PersonEntity: Name: NameFull: Si, Zhi-Guang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 7 Titles: – TitleFull: Engineering Letters Type: main |
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