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IJMLC 2020 Vol.10(3): 465-470 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.3.958

A Real-time Risk Assessment for Information System with CICIDS2017 Dataset Using Machine Learning

Preecha Pangsuban, Prachyanun Nilsook, and Panita Wannapiroon

Abstract—The purpose of this research was to study the concept and architectural design for Risk Assessment (RA) for information system with the Canadian Institute for Cybersecurity Intrusion Detection Systems 2017 dataset (CICIDS2017 dataset) using Machine Learning (ML) to establish a model. It evaluated the risk on detected network data. The results indicated, the concept consisted of input such as CICIDS2017 dataset, ML, network data and risk matrix. Information system real time RA using CICIDS2017 dataset and ML were processes and the RA on the system were outcomes. In addition, the concept components were improved upon and comprised of four sections; 1) network data capture for network data collection, 2) CICIDS2017 that was intrusion dataset for establishment of a predictive model with ML algorithm, 3) classification predictive model, forecasted on intrusion from network data and 4) RA report, estimated risk of information in risk matrix format. Finally, architectural design, consists of three major parts which includes; network data capture, risk predictive analysis and RA report.

Index Terms—Real time risk assessment, information system, CICIDS2017 dataset, machine learning.

The authors are with the Division of Information and Communication Technology for Education, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangkok, Thailand (e-mail: preecha@yru.ac.th, prachyanun.n@fte.kmutnb.ac.th, panita.w@fte.kmutnb.ac.th).


Cite: Preecha Pangsuban, Prachyanun Nilsook, and Panita Wannapiroon, "A Real-time Risk Assessment for Information System with CICIDS2017 Dataset Using Machine Learning," International Journal of Machine Learning and Computing vol. 10, no. 3, pp. 465-470, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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