Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
These days, everyone has a smartphone with basic features like messaging and calling. The frequent ringing of smartphones from spam calls is already famous for its fraudulent or commercial pitches to unsuspecting consumers. A sizable portion of these spam calls has switched to messaging due to network operators' lower-cost bulk messaging services. Short Message Service, or SMS, has developed into a hub for spam product descriptions and fake offers. This is where classification is required in this situation. In this context, classification refers to the process of separating spam messages from valid or invalid messages. Using a dataset from the UCI repository, we applied APPLICATION Programming Interface for live analysis of spam detection of message. At the conclusion, we compared the accuracy results, which indicated the best model.
Keywords:
Spam, Ham, UCI.
Cite Article:
"Smishing Detection ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.781 - 785, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304130.pdf
Downloads:
000205140
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator