An Improved Machine Learning-Based Short Message Service Spam Detection System
| dc.contributor.author | Oluwatoyin, Odukoya | |
| dc.contributor.author | Bodunde, Akinyemi | |
| dc.contributor.author | Titus, Gooding | |
| dc.contributor.author | Ganiyu, Aderounmu | |
| dc.date.accessioned | 2022-05-26T08:57:37Z | |
| dc.date.available | 2022-05-26T08:57:37Z | |
| dc.date.issued | 2019-11-12 | |
| dc.description.abstract | The use of Short Message Services (SMS) as a mechanism of communication has resulted to loss of sensitive information such as credit card details, medical information and bank account details (user name and password). Several Machine learning-based approaches have been proposed to address this problem, but they are still unable to detect modified SMS spam messages more accurately. Thus, in this research, a stack- ensemble of four machine learning algorithms consisting of Random Forest (RF), Logistic Regression (LR), Multilayer Perceptron (MLP), and Support Vector Machine (SVM), were employed to detect more accurately SMS spams. The simulation was carried out using Python Scikit- learn tools. | en_US |
| dc.identifier.other | 10.5815/ijcnis.2019.12.05 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/1451 | |
| dc.language.iso | en | en_US |
| dc.publisher | MECS | en_US |
| dc.title | An Improved Machine Learning-Based Short Message Service Spam Detection System | en_US |
| dc.type | Article | en_US |
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