arXiv (All)
open access
Johnny Still Receives Spam SMS: Assessing the Robustness of SMS Spam Detection
SMS spam detection systems often achieve high accuracy in controlled environments but struggle against adversarial attacks and increasingly sophisticated spam tactics in real-world deployments. In this paper, we evaluate the robustness of SMS anti-spam systems that end users actually rely on, includ
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Breaking and Defending LLM-Powered Social Media Bot Detection Systems#617022
- Breaking and Defending LLM-Powered Social Media Bot Detection Systems#616522
- Semantic information from smashed data: text reconstruction attack on phishing detection models in split learning#459157
- Combating web-based fake profiles using optimized LightGBM: an ensemble learning approach using OSEMN framework#390111
- Intelligent Detection of Fake Profiles on Social Media Using Machine Learning#102702
Record · ID 821139
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive