Publication2024
False Data Injection Attack Detection Using Machine Learning in Industrial Internet of Things
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- Optimized Computational Intelligence Driven Decision Making Theory Application and Challenges
- Pages
- 49-68
- Published
- 1 January 2024
- DOI
- 10.1002/9781394242566.ch3
- Scopus ID
- 85206203379
- Authors
- 3
- Open access
- No
- Citations
- 5
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Machine learning research on false data injection attack detection in industrial IoT systems, advancing cybersecurity innovation capacity.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
16.6 Develop effective, accountable and transparent institutions at all levels
Cybersecurity research addresses protection of critical infrastructure and institutional systems against malicious attacks.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-4.4 Public safety, cybersecurity and national security
Cybersecurity research for industrial systems protection contributes to national security and critical infrastructure resilience.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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Provenance
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “False Data Injection Attack Detection Using Machine Learning in Industrial Internet of Things”. Kortex.