Search results for "HTTP traffic"
showing 4 items of 4 documents
Efficiency Analysis Of Resource Request Patterns In Classification Of Web Robots And Humans
2018
The paper deals with the problem of classification of Web traffic generated by robots and humans on e-commerce websites. Due to the still growing proliferation and specialization of bots, a large body of research into characterization and recognition of their traffic has been conducted so far. In particular, some approaches to classify bot and human sessions on websites have been proposed in the literature. In this paper we verify and discuss the efficiency of such recently proposed approach, which uses differences in resource request patterns of bots and humans. We reconstructed Web sessions from actual HTTP log data for three different e-commerce sites, varying in the traffic intensity an…
HTTP-level e-commerce data based on server access logs for an online store
2020
Abstract Web server logs have been extensively used as a source of data on the characteristics of Web traffic and users’ navigational patterns. In particular, Web bot detection and online purchase prediction using methods from artificial intelligence (AI) are currently key areas of research. However, in reality, it is hard to obtain logs from actual online stores and there is no common dataset that can be used across different studies. Moreover, there is a lack of studies exploring Web traffic over a longer period of time, due to the unavailability of long-term data from server logs. The need to develop reliable models of Web traffic, Web user navigation, and e-customer behaviour calls for …
Verification of Web traffic burstiness and self-similarity for multiple online stores
2017
Developing realistic Web traffic models is essential for a reliable Web server performance evaluation. Very significant Web traffic properties that have been identified so far include burstiness and self-similarity. Very few relevant studies have been devoted to e-commerce traffic, however. In this paper, we investigate burstiness and self-similarity factors for seven different online stores using their access log data. Our findings show that both features are present in all the analyzed e-commerce datasets. Furthermore, a strong correlation of the Hurst parameter with the average request arrival rate was discovered (0.94). Estimates of the Hurst parameter for the Web traffic in the online …
Investigating Long-Range Dependence in E-Commerce Web Traffic
2016
This paper addresses the problem of investigating long-range dependence (LRD) and self-similarity in Web traffic. Popular techniques for estimating the intensity of LRD via the Hurst parameter are presented. Using a set of traces of a popular e-commerce site, the presence and the nature of LRD in Web traffic is examined. Our results confirm the self-similar nature of traffic at a Web server input, however the resulting estimates of the Hurst parameter vary depending on the trace and the technique used.