Web User Profiling Based on Browsing Behavior Analysis

Fan, Xiao-Xi; Chow, Kam-Pui; Xu, Fei

doi:10.1007/978-3-662-44952-3_5

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…While the field has advanced in big leaps, research in the various (sub-)domains also tend to drift apart. This is particularly the case for domains that deal with processing of sequential data, such as geo-locations [75], shopping paths [47], text [54], video streaming [46], [78], music [12], [95], clickstreams [15], internet browsing behavior [28], financial transactions [60], electronic health records [6] or water treatment. These data stem from dynamic phenomena which are at the heart of many fields of research, but they pose significant challenges for modelers and analysts.…”

Section: Introductionmentioning

confidence: 99%

Deep Generative Models for Synthetic Data: A Survey

et al. 2023

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A growing interest in synthetic data has stimulated the development and advancement of a large variety of deep generative models for a wide range of applications. However, as this research has progressed, its streams have become more specialized and disconnected from one another. This is why models for synthesizing text data for natural language processing cannot readily be compared to models for synthesizing health records anymore. To mitigate this isolation, we propose a data-driven evaluation framework for generative models for synthetic sequential data, an important and challenging sub-category of synthetic data, based on five high-level criteria: representativeness, novelty, realism, diversity and coherence of a synthetic data-set relative to the original data-set regardless of the models' internal structures. The criteria reflect requirements different domains impose on synthetic data and allow model users to assess the quality of synthetic data across models. In a critical review of generative models for sequential data, we examine and compare the importance of each performance criterion in numerous domains. We find that realism and coherence are more important for synthetic data natural language, speech and audio processing tasks. At the same time, novelty and representativeness are more important for healthcare and mobility data. We also find that measurement of representativeness is often accomplished using statistical metrics, realism by using human judgement, and novelty using privacy tests.

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Section: Introductionmentioning

confidence: 99%

Deep Generative Models for Synthetic Data: A Survey

et al. 2023

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“…Research on user identification in areas of behavioral inference (Fan et al 2014;Yang 2010;Adeyemi et al 2014), biometric dynamics (Ernsberger et al 2017;Ikuesan and Venter 2018;Ikuesan et al 2019), and network traffic analysis (Li et al 2013a;Melnikov and Schönwälder 2010a;Adeyemi et al 2016) are methods adapted for user identification through pattern extraction. The process employed for network traffic pattern extraction includes logs and media scavenging, mining of audit trails, client-side caching, and extraction of flow records from captured network traffic.…”

Section: Introductionmentioning

confidence: 99%

A heuristics for HTTP traffic identification in measuring user dissimilarity

Ikuesan

Salleh

Venter

et al. 2020

Hum.-Intell. Syst. Integr.

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The prevalence of HTTP web traffic on the Internet has long transcended the layer 7 classification, to layers such as layer 5 of the OSI model stack. This coupled with the integration-diversity of other layers and application layer protocols has made identification of user-initiated HTTP web traffic complex, thus increasing user anonymity on the Internet. This study reveals that, with the current complex nature of Internet and HTTP traffic, browser complexity, dynamic web programming structure, the surge in network delay, and unstable user behavior in network interaction, user-initiated requests can be accurately determined. The study utilizes HTTP request method of GET filtering, to develop a heuristic algorithm to identify user-initiated requests. The algorithm was experimentally tested on a group of users, to ascertain the certainty of identifying user-initiated requests. The result demonstrates that user-initiated HTTP requests can be reliably identified with a recall rate at 0.94 and F-measure at 0.969. Additionally, this study extends the paradigm of user identification based on the intrinsic characteristics of users, exhibited in network traffic. The application of these research findings finds relevance in user identification for insider investigation, e-commerce, and e-learning system as well as in network planning and management. Further, the findings from the study are relevant in web usage mining, where user-initiated action comprises the fundamental unit of measurement.

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