Publication2021
Web page XSS attack detection from malicious JavaScript using machine learning: A survey
The record
- Type
- Publication
- Date
- 1 January 2021
- Level
- National
- Journal
- 12th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2021
- Volume
- 2021-August
- Pages
- 648-653
- Published
- 1 January 2021
- Scopus ID
- 85117829531
- Authors
- 2
- 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
16.5 Substantially reduce corruption and bribery in all their forms
Machine learning detection of malicious JavaScript and XSS attacks strengthens cybersecurity and reduces fraud in web applications.
- 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.c Significantly increase access to ICT and provide universal and affordable access to the internet
Static analysis and ML classification techniques advance digital infrastructure security research.
- 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
XSS attack detection directly protects cybersecurity and prevents malware distribution through compromised web pages.
- 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. “Web page XSS attack detection from malicious JavaScript using machine learning: A survey”. Kortex.