Publication2022
Early Detection of Botnet Based Attacks Using Various Classification Techniques on Traffic Behavioral Features
The record
- Type
- Publication
- Date
- 1 January 2022
- Level
- National
- Journal
- Lecture Notes in Networks and Systems
- Volume
- 395
- Pages
- 217-225
- Published
- 1 January 2022
- DOI
- 10.1007/978-981-16-9480-6_20
- Scopus ID
- 85132018871
- ISSN
- 23673370
- Authors
- 3
- 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
Early detection of botnet attacks using machine learning classifiers represents cybersecurity research innovation and IoT network protection advancement.
- 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
Work on botnet and ransomware detection for IoT and wireless networks addresses cybersecurity and national security 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. “Early Detection of Botnet Based Attacks Using Various Classification Techniques on Traffic Behavioral Features”. Kortex.