Performance analysis of binary and multiclass models using azure machine learning

Smitha Rajagopal, Katiganere Siddaramappa Hareesha, Poornima Panduranga Kundapur

Research output: Contribution to journalArticle

Abstract

Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time.

Original languageEnglish
Pages (from-to)978-986
Number of pages9
JournalInternational Journal of Electrical and Computer Engineering
Volume10
Issue number1
DOIs
Publication statusPublished - 01-01-2020

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Learning systems
Intrusion detection
Learning algorithms
Testing

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

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abstract = "Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2{\%} accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99{\%}, 94.49{\%}, 91.79{\%} and 90.9{\%} respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time.",
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Performance analysis of binary and multiclass models using azure machine learning. / Rajagopal, Smitha; Hareesha, Katiganere Siddaramappa; Kundapur, Poornima Panduranga.

In: International Journal of Electrical and Computer Engineering, Vol. 10, No. 1, 01.01.2020, p. 978-986.

Research output: Contribution to journalArticle

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