πŸ“„
Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML paper by Independent / Community
⭐ 69
πŸ“„
Paper

Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML

by Independent / Community 0031e9cbc9568967755617493f525eb852baad2b
Free2AITools Nexus Index
69.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 84
P: Popularity 60
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Phishing, a well-known cyber-attack practice has gained significant research attention in the cyber-security domain for the last two decades due to its dynamic attacking strategies. Although different solutions have been exercised against phishing, phishing attacks have dramatically increased in the past few years. Recent studies have shown that machine learning has become prominent in the present anti-phishing context, and the techniques like deep learning have extensively improved anti-phis...

Source β†’
Semantic Scholar 52 Citations
Paper Information Summary
Entity Passport
Registry ID 0031e9cbc9568967755617493f525eb852baad2b
License ArXiv
Provider semantic_scholar
πŸ“œ

Cite this paper

Academic & Research Attribution

BibTeX 
@misc{0031e9cbc9568967755617493f525eb852baad2b,
  author = {Unknown},
  title = {Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0031e9cbc9568967755617493f525eb852baad2b}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML [Paper]. Free2AITools. https://api.semanticscholar.org/0031e9cbc9568967755617493f525eb852baad2b

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 84
Popularity (P) 60
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML: Authority (A:84), Popularity (P:60), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

πŸ“ Executive Summary

"Phishing, a well-known cyber-attack practice has gained significant research attention in the cyber-security domain for the last two decades due to its dynamic attacking strategies. Although different solutions have been exercised against phishing, phishing attacks have dramatically increased in the past few years. Recent studies have shown that machine learning has become prominent in the present anti-phishing context, and the techniques like deep learning have extensively improved anti-phis..."

❝ Cite Node 

@article{Unknown2026Combining,
  title={Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

πŸ”— Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

πŸ“Š Research Signals

πŸ“ˆ52CitationsSemantic Scholar
πŸ›οΈ84AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈautomation workflowField

🏷️ Research Topics

attention mechanism
πŸ“¦Data Source: semantic_scholar
πŸ”„ Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
2
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

downloads
0
stars
null
forks
null
citations
52

Data indexed from public sources. Updated daily.