10.46243/jst.2023.v8.i06.pp158-162 registered
Effective And Efficient Detection Of Phishing Emails Using Machine Learning
Resolves to https://www.jst.org.in/index.php/pub/article/view/757
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2023.v8.i06.pp158-162
Registered 29 Sep 2026 via crossref · record version 2 · last change 29 Sep 2026, 11:59 PM · record sha256 02c257279d96f266…
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What the DOI identifies
JournalArticle — an article in a journal · Digital · Visual · en
Effective And Efficient Detection Of Phishing Emails Using Machine Learning (PrincipalTitle)
Published 2023-08-07
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 8 · issue 7 · pages 158–162
Agents
- Moola.Akshitha Moola.Akshitha (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2023.v8.i06.pp158-162
Abstract
Emails are widely used for personal and professional communication,often involving the transmission of sensitive information like banking details,credit reports,and login data.Consequently,these emails become valuable targets for cyber criminals who seek to exploit such knowledge for their own malicious purposes.Phishing, a deceptive technique employed by these individuals,involves impersonating well-known sources to deceive and extract sensitive information from unsuspecting individuals.The sender of a phishing email uses false pretenses to persuade recipients into disclosed personal information.In this work,the detection of phishing emails is learning methods to categorize emails as either genuine or phishing attempts.LMT classifiers have proven highly effective in accurately classifying emails,achieving optimal accuracy in email classification tasks.
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.
| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jst.2023.v8.i06.pp158-162 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | Effective And Efficient Detection Of Phishing Emails Using Machine Learning (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: Moola.Akshitha Moola.Akshitha publisher: Longman Publishers published: 2023-08-07 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 7 · pp. 158–162 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2024-02-16 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
complete Every System Metadata element is here, with the basic metadata a journal article needs.
The System Metadata Declaration (JSON) · the Kernel Metadata Declaration (XML) · what each sub-type needs
History — the ledger
Every change to this DOI, in order, as it was recorded. Entries are only ever added, never changed or removed.
| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 80 fields set · sha256 46587361065b… |
| 2 | 29 Sep 2026, 11:59 PM | update record re-read from api.crossref.org | Administrator (admin) | agents.0.name.family: agents.0.name.given: container.titles.0.value: |
Machine-readable: the history as JSON, with the full record after each change.
