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Guide to Intrusion Detection and Prevention Systems (IDPS)
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The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the nation's measurement and standards infrastructure.ITL develops tests, test methods, reference data, proof of concept implementations, and technical analysis to advance the development and productive use of information technology.ITL's responsibilities include the development of technical, physical, administrative, and management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems.This Special Publication 800-series reports on ITL's research, guidance, and outreach efforts in computer security and its collaborative activities with industry, government, and academic organizations.Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately.Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose.
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“At the process layer, protection technologies like firewalls, intrusion detection systems, and role-based access controls are required to align with defined policies to provide concrete safeguards [7].”
From Seamless Industry 4.0 Integration: A Multilayered Cyber-Security Framework for Resilient SCADA Deployments in CPPS (2023) · cites this paper as [7]
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Use of the Common Vulnerabilities and Exposures (CVE) vulnerability naming scheme
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Self-citationIngenhoven, C. (2010) · De Gruyter eBooks · doi:10.11129/detail.9783955531713.18 · Cited by 4
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Metadata
- Authors
- Scarfone, K. A., & Mell, P. M.
- Affiliations
- National Institute of Standards and Technology (US)
- Published
- 2007
- OpenAlex
- W2678934292
- Cited by
- 1,279
- References
- 2
- Citations by year
- 2026: 81 · 2025: 92 · 2024: 64 · 2023: 56 · 2022: 61 · 2021: 63 · 2020: 70 · 2019: 86 · 2018: 77 · 2017: 78 · 2016: 84 · 2015: 76 · 2014: 82 · 2013: 91 · 2012: 71
- Type
- report
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Funders
- U.S. Department of State
- Cisco Systems
- National Institute of Standards and Technology
- Army Research Laboratory
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- intrusion detection systems, intrusion prevention systems, network security, cybersecurity, security testing, security standards
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