Smart User Consumption Analysis Using Support Vector Machine and Multilayer Perceptron
Madugu Jeremiah Omanga
Department of Computer Science, Taraba State University, Jalingo, Nigeria.
Olajide Blessing Olajide *
Department of Computer Engineering, Federal University, Wukari, Nigeria.
Okpor James
Department of Computer Engineering, Federal University, Wukari, Nigeria.
Andrew Ishaku Wreford
Department of Computer Science, Federal University, Wukari, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
The advent of smart devices and the rapid growth of wireless and wired networks has brought about a swift change in the methods of entertainment and information sources. This rapid expansion is due to the rise in over-the-top (OTT) usage, among which are the use of social networks, gaming, online video, and audio applications. OTT delivers audio, video, and other media content over the internet generating high traffic on the network infrastructure. Degradation of service is a typical tactic used to address excessive consumption patterns of smart users. However, it is deficient in that, rarely is the analysis of consumer behavior taken into consideration. Analysis of user’s consumption habits before the degradation of service is necessary for the efficiency of this mechanism. This study proposes a smart user consumption analysis using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). Real-world traffic data collected in the network from Universidad Del Cauca, Popayán with a total of 2,704,839 flow instances was used. SVM and MLP were used in this study to analyze and classify user consumption behavior on OTT applications since user consumption behavior in real-world networks changes over time. The experimental result of this study showed that the two models performed extremely well in the analysis and classification of OTT application users. Nevertheless, MLP out performed SVM in both precision and F1-score. The evaluation metrics used are Recall, Precision, and F1-score based on True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN).
Keywords: Smart devices, OTT application, network, degradation, traffic, SVM, MLP
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References
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