Impact of Number of Features Selected and Size of Training Data on the Accuracy of Machine Learning Based Cloud Security Algorithms – An Empirical Analysis


  • Tanko Yahaya Mohammed Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Abdulrazaq Abdulrahim Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Muhammad Aminu Umar Dept. Computer Science, Ahmadu Bello University, Zaria, Nigeria
  • Aliyu Muhammad Kufena Department of Computer Science Ahmadu Bello University Zaria, Nigeria.
  • Hadiza Isa Abdullahi Department of Computer Science Ahmadu Bello University Zaria, Nigeria.


Empirical Analysis, Accuracy Prediction, Features Selected, Training Data


This study uses Ordinary Least Square (OLS) regression to investigate if the number of features selected and the size of training data are useful in predicting the accuracies obtained in ML based approaches to cloud security.


Abusitta, A., Bellaiche, M., Dagenais, M., & Halabi, T. (2019). A deep learning approach for proactive multi-cloud cooperative intrusion detection system. Future Generation Computer Systems, 98, 308–318.

Alrawashdeh, K., & Purdy, C. (2017). Toward an online anomaly intrusion detection system based on deep learning. Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, 195–200.

Barbedo, J. G. A. (2018). Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computers and Electronics in Agriculture, 153(July), 46–53.

Chen, X., & Qian, W. (2020). Effect of marine environmental regulation on the industrial structure adjustment of manufacturing industry: An empirical analysis of China’s eleven coastal provinces. Marine Policy, 113(December 2019), 103797.

Chiba, Z., Abghour, N., Moussaid, K., El omri, A., & Rida, M. (2019). Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms. Computers and Security, 86, 291–317.

Cui, Z., & Gong, G. (2018). The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. NeuroImage, 178(February), 622–637.

Díaz, E., Panach, J. I., Rueda, S., Ruiz, M., & Pator, O. (2021). Are requirements elicitation sessions influenced by participants’ gender? An empirical experiment. Science of Computer Programming, 204, 102595.

Dutta, S., & Gros, E. (2018). Evaluation of the impact of deep learning architectural components selection and dataset size on a medical imaging task. 1057911(March 2018), 36.

Geetha, M., Singha, P., & Sinha, S. (2017). Relationship between customer sentiment and online customer ratings for hotels - An empirical analysis. Tourism Management, 61, 43–54.