Publication2026
Smart Cybersecurity: A Dataset-Driven Comparative Analysis of ML Techniques for Network Intrusion Detection and Prevention
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- Proceedings International Conference on Air Defense and Security Icads 2026
- Published
- 1 January 2026
- DOI
- 10.1109/icads69450.2026.11545500
- Scopus ID
- 105042682031
- Authors
- 4
- Open access
- No
- Citations
- 0
- 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
Comparative analysis of machine learning classifiers (XGBoost, LightGBM, CatBoost) for network intrusion detection demonstrates effective threat detection on imbalanced datasets
- 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-3.2 Digital technology and artificial intelligence
Cybersecurity and machine learning innovation strengthens digital technology 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. “Smart Cybersecurity: A Dataset-Driven Comparative Analysis of ML Techniques for Network Intrusion Detection and Prevention”. Kortex.