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Guide to integrating forensic techniques into incident response
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Reports on Computer Systems TechnologyThe 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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“A single undetected modification can invalidate an entire chain of evidence and compromise legal admissibility (Kent et al., 2006; International Organization for Standardization, 2012; Stoyanova et al., 2020).”
From Lightweight Tamper-Evident Log Integrity Verification for IoT Edge Environments: A Merkle Tree Pipeline with Adaptive Chunking (2026) · cites this paper as Kent et al., 2006
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“When these logs serve as evidence in forensic investigations, compliance audits, or incident-response workflows, their integrity is not merely desirable; it is a hard prerequisite (Kebande and Venter, 2017; Cichonski et al., 2012; Kent et al., 2006; International Organization for Standardization, 2012).”
From Lightweight Tamper-Evident Log Integrity Verification for IoT Edge Environments: A Merkle Tree Pipeline with Adaptive Chunking (2026) · cites this paper as Kent et al., 2006
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Research works printed but not linked: 2
- Honeynet Project. Know Your Enemy: Learning about Security Threats, Second Edition.
- Prosise, Chris, et al. Incident Response and Computer Forensics, Second Edition. McGraw-Hill Osborne Media, 2003.
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Akremi, A., Sallay, H., Rouached, M., & Bouaziz, R. (2019) · International Journal of Web Services Research · doi:10.4018/ijwsr.2020010102 · Cited by 11
FFaizal, A., & Luthfi, A. (2024) · Journal of Information Systems and Informatics · doi:10.51519/journalisi.v6i2.717 · Cited by 10
Hariyadi, D., Kusuma, M., Sholeh, A., & Fazlurrahman (2021) · Advances in engineering research/Advances in Engineering Research · doi:10.2991/aer.k.211222.023 · Cited by 10
Metadata
- Authors
- Kent, K., Chevalier, S., Grance, T., & Dang, H.
- Affiliations
- National Institute of Standards and Technology (US)
- Published
- 2006
- OpenAlex
- W2628759714
- Cited by
- 590
- References
- 14
- Citations by year
- 2026: 33 · 2025: 32 · 2024: 31 · 2023: 36 · 2022: 34 · 2021: 37 · 2020: 52 · 2019: 49 · 2018: 35 · 2017: 40 · 2016: 39 · 2015: 38 · 2014: 38 · 2013: 43 · 2012: 24
- Type
- report
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Topics
- Physical Sciences › Computer Science › Information Systems › Digital and Cyber Forensics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Social Sciences › Law › Education, Law, and Society
- Keywords
- forensic computing, digital forensics, incident response, digital evidence, cybersecurity
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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