A Stacked Ensemble Framework for Detecting Malicious Insiders

Authors

  • Abolaji B Akanbi Department of Computer Science, Babcock University, Ogun State, Nigeria, Author
  • Adewale O Adebayo Department of Information Technology, Babcock University, Ogun State, Nigeria Sunday A. Idowu, Department of Software Engineering, Babcock University, Ogun State, Nigeria Author
  • Ebunoluwa E Okediran Department of Computer Science, Babcock University, Ogun State, Nigeria Author

Keywords:

Ensemble Learning, Malicious Insider Threat, Machine Learning, Stacked Generalization

Abstract

One of the mainstream strategies identified  for detecting Malicious Insider Threat (MIT) is building  stacking ensemble Machine Learning (ML) models to  reveal malevolent insider activities through anomalies in  user activities. However, most anomalies found by these  learning models were not malicious because MIT was  treated as a single entity, whereas there are various forms of  this threat with their own distinct signature. To address this  deficiency, this study focused on designing a stacked  ensemble framework for detecting malicious insider threat which utilizes a one scenario per algorithm strategy. A  model that can be used to test the framework was proposed. 

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References

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Published

2020-07-04

Issue

Vol. 8 No. 4 (2020): International Journal of Innovative Research in Computer Science and Technology (Jul) 2020

Section

Articles

How to Cite

A Stacked Ensemble Framework for Detecting Malicious Insiders. (2020). International Journal of Innovative Research in Computer Science & Technology, 8(4), 294–298. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13237