Publication2021
An Ensemble-based Outlier Detection Approach on Intrusion Detection
The record
- Type
- Publication
- Date
- 1 January 2021
- Level
- National
- Journal
- Proceedings 2021 19th Oits International Conference on Information Technology Ocit 2021
- Pages
- 404-409
- Published
- 1 January 2021
- DOI
- 10.1109/ocit53463.2021.00085
- Scopus ID
- 85127400162
- Authors
- 3
- Open access
- No
- Citations
- 4
- 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 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Ensemble-based outlier detection combining local outlier factor, isolation forest and one-class SVM improves intrusion detection system accuracy by removing training data anomalies.
- 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
Viksit Bharat @2047
vb-4.4 Public safety, cybersecurity and national security
Intrusion detection system advancement supports cybersecurity and national security through network 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 Outlier Detection Approach on Intrusion Detection”. Kortex.