Intrusion Detection System Using Data Stream Classification

  • Amer Abdulmajeed Abdualrahman Informatics Institute for Post Graduate Studies, College of Science, University of Baghdad, Baghdad, Iraq
  • Mahmood Khalel Ibrahem College of Information Engineering, Al-Nahrain University
Keywords: Intrusion Detection System, Data Stream Classification, CICIDS 2017 dataset, Feature selection


Secure data communication across networks is always threatened with intrusion and abuse. Network Intrusion Detection System (IDS) is a valuable tool for in-depth defense of computer networks. Most research and applications in the field of intrusion detection systems was built based on analysing the several datasets that contain the attacks types using the classification of batch learning machine. The present study presents the intrusion detection system based on Data Stream Classification. Several data stream algorithms were applied on CICIDS2017 datasets which contain several new types of attacks. The results were evaluated to choose the best algorithm that satisfies high accuracy and low computation time.

How to Cite
Abdualrahman, A. A., & Ibrahem, M. K. (2021). Intrusion Detection System Using Data Stream Classification. Iraqi Journal of Science, 62(1), 319-328.
Computer Science