Artificial Intelligence Web Application Firewall for advanced detection of web injection attacks.
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| Title: | Artificial Intelligence Web Application Firewall for advanced detection of web injection attacks. |
|---|---|
| Authors: | Román‐Gallego, Jesús‐Ángel1 (AUTHOR) zjarg@usal.es, Pérez‐Delgado, María‐Luisa1 (AUTHOR), Viñuela, Marcos Luengo1 (AUTHOR), Vega‐Hernández, María‐Concepción1 (AUTHOR) |
| Source: | Expert Systems. Jan2025, Vol. 42 Issue 1, p1-18. 18p. |
| Subjects: | HTTP (Computer network protocol), Web-based user interfaces, Artificial intelligence, Support vector machines, Internet servers, K-nearest neighbor classification |
| Abstract: | Currently, web services‐based applications have an important presence in public and private organizations. The vulnerabilities that these types of applications may have pose an inherent potential risk to the business model of these organizations. These applications have the inherent risk of being used by organizations in such a way that their activity is affected and they become the main entry point for attackers who want to breach their security. The main barrier to this type of attack are web application firewalls (WAF), which are responsible for processing Hypertext Transfer Protocol requests between clients and web servers, classifying them and rejecting malicious requests. This type of (WAF) applications, for the most part, have regular expressions that correspond to general rules and allow detecting malicious requests that follow a pattern contained in them. However, due to the knowledge of these rules by attackers, it is easy to circumvent security and to impersonate a malicious request by an innocuous request. Therefore, in this article, we present a study of different models based on artificial intelligence techniques as Naïve Bayes, k‐nearest neighbors, support vector machines, and linear regression to test their effectiveness in detecting malicious requests from a synthetic dataset containing more than 100,000 requests. The results obtained show that the implementation of these methods optimize the detection of malicious requests obtaining results between 92% and 99% of success in their classification. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems is the property of Wiley-Blackwell 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: 181701545 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial Intelligence Web Application Firewall for advanced detection of web injection attacks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Román‐Gallego%2C+Jesús‐Ángel%22">Román‐Gallego, Jesús‐Ángel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zjarg@usal.es</i><br /><searchLink fieldCode="AR" term="%22Pérez‐Delgado%2C+María‐Luisa%22">Pérez‐Delgado, María‐Luisa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Viñuela%2C+Marcos+Luengo%22">Viñuela, Marcos Luengo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vega‐Hernández%2C+María‐Concepción%22">Vega‐Hernández, María‐Concepción</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems%22">Expert Systems</searchLink>. Jan2025, Vol. 42 Issue 1, p1-18. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22HTTP+%28Computer+network+protocol%29%22">HTTP (Computer network protocol)</searchLink><br /><searchLink fieldCode="DE" term="%22Web-based+user+interfaces%22">Web-based user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+servers%22">Internet servers</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Currently, web services‐based applications have an important presence in public and private organizations. The vulnerabilities that these types of applications may have pose an inherent potential risk to the business model of these organizations. These applications have the inherent risk of being used by organizations in such a way that their activity is affected and they become the main entry point for attackers who want to breach their security. The main barrier to this type of attack are web application firewalls (WAF), which are responsible for processing Hypertext Transfer Protocol requests between clients and web servers, classifying them and rejecting malicious requests. This type of (WAF) applications, for the most part, have regular expressions that correspond to general rules and allow detecting malicious requests that follow a pattern contained in them. However, due to the knowledge of these rules by attackers, it is easy to circumvent security and to impersonate a malicious request by an innocuous request. Therefore, in this article, we present a study of different models based on artificial intelligence techniques as Naïve Bayes, k‐nearest neighbors, support vector machines, and linear regression to test their effectiveness in detecting malicious requests from a synthetic dataset containing more than 100,000 requests. The results obtained show that the implementation of these methods optimize the detection of malicious requests obtaining results between 92% and 99% of success in their classification. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell 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: Identifiers: – Type: doi Value: 10.1111/exsy.13505 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: HTTP (Computer network protocol) Type: general – SubjectFull: Web-based user interfaces Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Internet servers Type: general – SubjectFull: K-nearest neighbor classification Type: general Titles: – TitleFull: Artificial Intelligence Web Application Firewall for advanced detection of web injection attacks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Román‐Gallego, Jesús‐Ángel – PersonEntity: Name: NameFull: Pérez‐Delgado, María‐Luisa – PersonEntity: Name: NameFull: Viñuela, Marcos Luengo – PersonEntity: Name: NameFull: Vega‐Hernández, María‐Concepción IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02664720 Numbering: – Type: volume Value: 42 – Type: issue Value: 1 Titles: – TitleFull: Expert Systems Type: main |
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