Publication2024
A Hybrid Reinforcement Learning Framework for Dynamic Resource Allocation in Malware Analysis Systems
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- International — held abroad
- Journal
- International Conference on Intelligent Algorithms for Computational Intelligence Systems Iacis 2024
- Published
- 1 January 2024
- DOI
- 10.1109/iacis61494.2024.10721700
- Scopus ID
- 85208802827
- Authors
- 6
- Open access
- No
- Citations
- 4
- International co-authorship
- Yes
- Co-author countries
- CN, GB, 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
Reinforcement learning agent dynamically allocates computational resources for malware analysis achieving 96.1% accuracy with adaptive threat level estimation capability
- 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
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
Cybersecurity and artificial intelligence innovation for evolving threat response and continuous learning systems
- 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. “A Hybrid Reinforcement Learning Framework for Dynamic Resource Allocation in Malware Analysis Systems”. Kortex.
