Publication2021
An Ensemble-Based Scalable Approach for Intrusion Detection Using Big Data Framework
The record
- Type
- Publication
- Date
- 1 August 2021
- Level
- National
- Journal
- Big Data
- Volume
- 9
- Issue
- 4
- Pages
- 303-321
- Published
- 1 August 2021
- DOI
- 10.1089/big.2020.0201
- Scopus ID
- 85113269747
- ISSN
- 21676461
- Authors
- 5
- Open access
- No
- Citations
- 39
- 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
16.4 Significantly reduce illicit financial and arms flows and combat all forms of organized crime
Ensemble-based machine learning using K-Means, RUSBoost and Decision Trees detects network intrusions with improved accuracy on imbalanced security datasets
- 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.
- 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
Viksit Bharat @2047
vb-4.4 Public safety, cybersecurity and national security
Cybersecurity intrusion detection through big data analytics contributes to national security and critical infrastructure protection
- 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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- 1
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Citing this record
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KIIT Deemed to be University. “An Ensemble-Based Scalable Approach for Intrusion Detection Using Big Data Framework”. Kortex.