Detecting spam in emails: "A comparative analysis of machine learning algorithms"
The fast-growing association of spam with scams, phishing, and malware makes them a major security threat to both individuals and organizations. Before machine learning-based spam filters were still efficient and could reach strong performance measures, the main point here is that these filters need to be more robust to issues like dataset shifts and adversarial manipulations. This research digs through the most popular machine learning algorithms (Naive et al. (SVM), Random Forest, and Long Short-Term Memory (LSTM)) in the case of spam detection. Experimental results show the link between accuracy, scalability, and computational efficiency, which implies that the algorithms should be flexible enough for the real world.
"Detecting spam in emails: "A comparative analysis of machine learning algorithms"", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 12, page no.a44-a47, December-2024, Available :https://ijsdr.org/papers/IJSDR2412005.pdf
Volume 9
Issue 12,
December-2024
Pages : a44-a47
Paper Reg. ID: IJSDR_212801
Published Paper Id: IJSDR2412005
Downloads: 000347126
Research Area: Information Technology
Country: mumbai,andheri east, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
