Abstract
Big data and cloud computing technology appeared on the scene as new trends due to the rapid growth of social media usage over the last decade. Big data represent the immense volume of complex data that show more details about behaviours, activities, and events that occur around the world. As a result, big data analytics needs to access diverse types of resources within a decreased response time to produce accurate and stable business experimentation that could help make brilliant decisions for organizations in real-time. These developments have spurred a revolutionary transformation in research, inventions, and business marketing. User behaviour analysis for classification and prediction is one of the hottest topics in data science. This type of analysis is performed for several purposes, such as finding users’ interests about a product (for marketing, e-commerce, etc.) or toward an event (elections, championships, etc.) and observing suspicious activities (security and privacy) based on their traits over the Internet. In this paper, a neuro-fuzzy approach for the classification and prediction of user behaviour is proposed. A dataset, composed of users’ temporal logs containing three types of information, namely, local machine, network and web usage logs, is targeted. To complement the analysis, each user’s 360-degree feedback is also utilized. Various rules have been implemented to address the company’s policy for determining the precise behaviour of a user, which could be helpful in managerial decisions. For prediction, a Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained based on the example set generated by a Fuzzy Rule Based System (FRBS) and the 360-degree feedback of the user. The results are obtained and compared with other state-of-the-art schemes in the literature, and the scheme is found to be promising in terms of classification as well as prediction accuracy.
Highlights
Online user behaviour analysis is an important area of research that enables different characteristics of users to be studied
An analysis of the characteristics of online user behaviour models is studied in [9], and the results show that feature extraction techniques, such as principle component analysis (PCA), independent component analysis (ICA) and self-organizing maps (SOM), can be used to correctly detect anomalies in user behaviour
Many algorithms, such as Fuzzy Rule Based System (FRBS) [20], Association Rule Mining, Linear Regression [21], REPTree [22], etc., are employed to classify user behaviour depending on their past web usage activities, and in turn, it helps in maintaining network security and privacy
Summary
Online user behaviour analysis is an important area of research that enables different characteristics of users to be studied. This paper proposes an automated monitoring and prediction tool, for organizations where there are restrictions on web usage or network access, i.e. each user is given certain privileges and is restricted from certain accesses. Martinelli et al [12] designed a deep learning classifier on a recent dataset to address this issue and experimentally showed the effectiveness of the model by achieving encouraging results Another investigation is made by [13] based on user behaviour within a large e-commerce site for predicting the buying intention of customers. They proposed a model consisting of Deep Belief Networks and Stacked Denoising auto-Encoders and showed that the extraction of features from high-dimensional data achieves a substantial improvement. The study of human behaviour characteristics on social media based on their post and reply behaviours at different times of a day [18] on online forums has revealed some interesting features that are (2019) 8:17
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
