Publication2022
Predicting Phishing URL Using Filter based Univariate Feature Selection Technique
The record
- Type
- Publication
- Date
- 1 January 2022
- Level
- National
- Journal
- 2022 2nd International Conference on Computer Science Engineering and Applications Iccsea 2022
- Published
- 1 January 2022
- DOI
- 10.1109/iccsea54677.2022.9936298
- Scopus ID
- 85142363901
- Authors
- 3
- Open access
- No
- Citations
- 5
- 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 Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels
Phishing URL detection using filter-based feature selection and machine learning achieves 99.8% accuracy, directly supporting cybersecurity and public safety.
- 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
9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Machine learning methodology application to cybersecurity represents research innovation in digital security technologies.
- 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.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-4.4 Public safety, cybersecurity and national security
Cybersecurity threat detection system supports national security and protection against digital attacks.
- 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.
Evidence
Kortex hosts no documents. Evidence is an outbound link to the authority that holds it.
No outbound evidence link is recorded for this activity. The record below states which source it came from and when it was last read.
Provenance
Which source system this record came from, how many source rows were resolved into it, and when each was last read.
- Source rows resolved into this record
- 1
Each record carries the date Kortex last read it from its source system. That is a freshness date and not a verification: Kortex records what its sources say and when it read them, and it does not check those statements against any external authority. No claim on this site is described as verified, because none is.
Citing this record
This URL is stable. A record’s permalink is frozen when it is published and is not regenerated if its title is later corrected.
KIIT Deemed to be University. “Predicting Phishing URL Using Filter based Univariate Feature Selection Technique”. Kortex.