Academic Journal
An Overview of EDR Serviceability for Security Information and Event Management (SIEM).
| Τίτλος: | An Overview of EDR Serviceability for Security Information and Event Management (SIEM). |
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| Συγγραφείς: | Pradhan, Padma Lochan |
| Πηγή: | Journal of Information Assurance & Security; 2026, Vol. 21 Issue 2, p52-88, 37p |
| Θεματικοί όροι: | Anomaly detection (Computer security), Machine learning, Internet security, Data analytics, Open source software, Artificial intelligence |
| Περίληψη: | This review paper focuses on and addresses Endpoint Detection and Response (EDR) tools, providing an overview of their purpose, functions, operations, services, and benefits within the cybersecurity landscape. Specifically, EDR solutions are designed to detect, prevent, investigate, and respond to advanced cyber threats that often bypass traditional antivirus programs. To achieve this, these tools continuously collect and analyze data in real time using behavioral analytics, artificial intelligence (AI), and machine learning (ML). This enables the identification of anomalous activities and sophisticated attack patterns, such as zero-day exploits and fileless malware. Furthermore, the integration of open-source tools strengthens an organization's security posture by enhancing service capabilities, scalability, reliability, and availability. The paper also discusses the evolution of EDR from standalone tools to integrated, interoperable, automated, and intelligence-driven platforms that utilize behavioral and predictive analysis to counter increasingly sophisticated threats. Such integration, in turn, enables faster decision-making while reducing code complexity, operational costs, and response times. Ultimately, the sustainability of open-source tools contributes to higher quality, improved performance, effective cost management, better decision-making, and reduced risk. In summary, this review synthesizes key developments and innovations in the EDR-SIEM domain, drawing from academic research, industry analysis, and real-world applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Information Assurance & Security is the property of Paradigm Publishing Services 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=15541010&ISBN=&volume=21&issue=2&date=20260605&spage=52&pages=52-88&title=Journal of Information Assurance & Security&atitle=An%20Overview%20of%20EDR%20Serviceability%20for%20Security%20Information%20and%20Event%20Management%20%28SIEM%29.&aulast=Pradhan%2C%20Padma%20Lochan&id=DOI:10.2478/ias-2026-0004 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: An Overview of EDR Serviceability for Security Information and Event Management (SIEM). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pradhan%2C+Padma+Lochan%22">Pradhan, Padma Lochan</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Information Assurance & Security; 2026, Vol. 21 Issue 2, p52-88, 37p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Open+source+software%22">Open source software</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This review paper focuses on and addresses Endpoint Detection and Response (EDR) tools, providing an overview of their purpose, functions, operations, services, and benefits within the cybersecurity landscape. Specifically, EDR solutions are designed to detect, prevent, investigate, and respond to advanced cyber threats that often bypass traditional antivirus programs. To achieve this, these tools continuously collect and analyze data in real time using behavioral analytics, artificial intelligence (AI), and machine learning (ML). This enables the identification of anomalous activities and sophisticated attack patterns, such as zero-day exploits and fileless malware. Furthermore, the integration of open-source tools strengthens an organization's security posture by enhancing service capabilities, scalability, reliability, and availability. The paper also discusses the evolution of EDR from standalone tools to integrated, interoperable, automated, and intelligence-driven platforms that utilize behavioral and predictive analysis to counter increasingly sophisticated threats. Such integration, in turn, enables faster decision-making while reducing code complexity, operational costs, and response times. Ultimately, the sustainability of open-source tools contributes to higher quality, improved performance, effective cost management, better decision-making, and reduced risk. In summary, this review synthesizes key developments and innovations in the EDR-SIEM domain, drawing from academic research, industry analysis, and real-world applications. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Information Assurance & Security is the property of Paradigm Publishing Services 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: 10.2478/ias-2026-0004 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 37 StartPage: 52 Subjects: – SubjectFull: Anomaly detection (Computer security) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Internet security Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Open source software Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: An Overview of EDR Serviceability for Security Information and Event Management (SIEM). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pradhan, Padma Lochan IsPartOfRelationships: – BibEntity: Dates: – D: 05 M: 06 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15541010 Numbering: – Type: volume Value: 21 – Type: issue Value: 2 Titles: – TitleFull: Journal of Information Assurance & Security Type: main |
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