International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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An Improved Machine Learning-Based Short Message Service Spam Detection System

  • Odukoya Oluwatoyin (Department of Computer Science and Engineering, Obafemi Awolowo University) ;
  • Akinyemi Bodunde (Department of Computer Science and Engineering, Obafemi Awolowo University) ;
  • Gooding Titus (Department of Computer Science and Engineering, Obafemi Awolowo University) ;
  • Aderounmu Ganiyu (Department of Computer Science and Engineering, Obafemi Awolowo University)
  • 투고 : 2024.10.05
  • 발행 : 2024.10.30
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초록

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. The performance evaluation of the proposed model was carried out by benchmarking it with an existing model. The evaluation results showed that the proposed model has an increase of 3.03% of accuracy, 8.94% of Recall, 2.17% of F-measure; and a decrease of 4.55% of Precision over the existing model. In conclusion, the ensemble method performed better than any individual algorithms and can be adopted by the Network service providers for better Quality of Service.

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과제정보

This Research was funded by the TETFund Research Fund" and Africa Centre of Excellence OAK-Park.

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