Machine Learning in Cybersecurity: Evaluating Text Encoding Techniques for Optimized SMS Spam Detection
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48573-2_25
Reference7 articles.
1. Amin, I., Dubey, M.K.: Hybrid ensemble and soft computing approaches for review spam detection on different spam datasets. Mater. Today Proc. 62, 4779–4787 (2022); International Conference on Innovative Technology for Sustainable Development
2. Sjarif, N.N.A., Azmi, N.F.M., Chuprat, S., Sarkan, H.M., Yahya, Y., Sam, S.M.: SMS spam message detection using term frequency-inverse document frequency and random forest algorithm. Procedia Comput. Sci. 161, 509-515 (2019)
3. The Fifth Information Systems International Conference, 23-24 July 2019, Surabaya, Indonesia
4. Kim, D., Seo, D., Cho, S., Kang, P.: Multi-co-training for document classification using various document representations: Tf–idf, lda, and doc2vec. Inf. Sci. 477, 15–29 (2019)
5. Magdy, S., Abouelseoud, Y., Mikhail, M.: Efficient spam and phishing emails filtering based on deep learning. Comput. Netw. 206, 108826 (2022)
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