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All Journal International Journal of Electrical and Computer Engineering
Poornima Panduranga Kundapur
Manipal Academy of Higher Education
Computer Science & IT Electrical & Electronics Engineering

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Performance analysis of binary and multiclass models using azure machine learning Smitha Rajagopal; Katiganere Siddaramappa Hareesha; Poornima Panduranga Kundapur
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (418.108 KB) | DOI: 10.11591/ijece.v10i1.pp978-986

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.
Co-Authors Katiganere Siddaramappa Hareesha Smitha Rajagopal