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Paper

An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework

by Independent / Community 00100b254519d8923c6d2a8757b5e1b6cb705a8a

Free2AITools Nexus Index

68.8

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 —

Malicious advertisement URLs pose a security risk since they are the source of cyber-attacks, and the need to address this issue is growing in both industry and academia. Several attempts have been made in recent years for malicious URL detection using machine learning (ML). The most widely used techniques extract linguistic features of URL string to extract features like bag-of-words (BoW) before applying ML model. Existing malicious URL detection techniques require effective manual feature ...

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Semantic Scholar 49 Citations

Paper Information Summary
Entity Passport
Registry ID	00100b254519d8923c6d2a8757b5e1b6cb705a8a
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{00100b254519d8923c6d2a8757b5e1b6cb705a8a,
  author = {Unknown},
  title = {An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00100b254519d8923c6d2a8757b5e1b6cb705a8a}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework [Paper]. Free2AITools. https://api.semanticscholar.org/00100b254519d8923c6d2a8757b5e1b6cb705a8a

🔬Technical Deep Dive

Full Specifications [+]

⚖️ 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

💬 Index Insight

FNI V2.0 for An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework: Authority (A:84), Popularity (P:60), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Malicious advertisement URLs pose a security risk since they are the source of cyber-attacks, and the need to address this issue is growing in both industry and academia. Several attempts have been made in recent years for malicious URL detection using machine learning (ML). The most widely used techniques extract linguistic features of URL string to extract features like bag-of-words (BoW) before applying ML model. Existing malicious URL detection techniques require effective manual feature ..."

❝ Cite Node

@article{Unknown2026An,
  title={An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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📊 Research Signals

📈49CitationsSemantic Scholar

🏛️84AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️automation workflowField

📦Data Source: semantic_scholar

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

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🆔 Identity & Source

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

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

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downloads: 0
stars: null
forks: null
citations: 49

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