Performance analysis of binary and multiclass models using azure machine learning

Rajgopal, S and Hareesha, K S and Kundapur, Poornima Panduranga (2020) Performance analysis of binary and multiclass models using azure machine learning. International Journal of Electrical and Computer Engineering, 10 (1). pp. 978-986. ISSN 2088-8708

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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.

Item Type: Article
Uncontrolled Keywords: Azure machine learning Decision forest Intrusion detection Locally deep SVM Mutual information UNSW NB-15
Subjects: Engineering > MIT Manipal > MCA
Depositing User: MIT Library
Date Deposited: 25 Sep 2020 08:57
Last Modified: 25 Sep 2020 08:57

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