Search results for "Web traffic"
showing 10 items of 14 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…
Computer networks stability independence of the queuing delays
2015
Communication in intelligent computer networks is an indispensible attribute of the dataflow quality in Web traffic. We propose a model that investigates intelligent computer networks stability while specifying its limits. Packet queuing delay affects the performance of the network, and especially its stability. If the network is presented as a dynamic system in block diagram form, we compute a transfer function and determine the quasi-polynomial system. The characteristic polynomial distribution of zeros of complex variable quasi-plane determines the boundaries of the network stability. The approach relies on estimation of the network system's transfer functions and its quasi-polynomial. C…
Bot recognition in a Web store: An approach based on unsupervised learning
2020
Abstract Web traffic on e-business sites is increasingly dominated by artificial agents (Web bots) which pose a threat to the website security, privacy, and performance. To develop efficient bot detection methods and discover reliable e-customer behavioural patterns, the accurate separation of traffic generated by legitimate users and Web bots is necessary. This paper proposes a machine learning solution to the problem of bot and human session classification, with a specific application to e-commerce. The approach studied in this work explores the use of unsupervised learning (k-means and Graded Possibilistic c-Means), followed by supervised labelling of clusters, a generative learning stra…
Detection of Internet robots using a Bayesian approach
2015
A large part of Web traffic on e-commerce sites is generated not by human users but by Internet robots: search engine crawlers, shopping bots, hacking bots, etc. In practice, not all robots, especially the malicious ones, disclose their identities to a Web server and thus there is a need to develop methods for their detection and identification. This paper proposes the application of a Bayesian approach to robot detection based on characteristics of user sessions. The method is applied to the Web traffic from a real e-commerce site. Results show that the classification model based on the cluster analysis with the Ward's method and the weighted Euclidean metric is very effective in robot det…
Analysis of Aggregated Bot and Human Traffic on E-Commerce Site
2014
A significant volume of Web traffic nowadays can be attributed to robots. Although some of them, e.g., search-engine crawlers, perform useful tasks on a website, others may be malicious and should be banned. Consequently, there is a growing need to identify bots and to characterize their behavior. This paper investigates the share of bot-generated traffic on an e-commerce site and studies differences in bots' and humans' session-based traffic by analyzing data recorded in Web server log files. Results show that both kinds of sessions reveal different characteristics, including the session duration, the number of pages visited in session, the number of requests, the volume of data transferre…
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 …
Modeling a non-stationary bots’ arrival process at an e-commerce Web site
2017
Abstract The paper concerns the issue of modeling and generating a representative Web workload for Web server performance evaluation through simulation experiments. Web traffic analysis has been done from two decades, usually based on Web server log data. However, while the character of the overall Web traffic has been extensively studied and modeled, relatively few studies have been devoted to the analysis of Web traffic generated by Internet robots (Web bots). Moreover, the overwhelming majority of studies concern the traffic on non e-commerce websites. In this paper we address the problem of modeling a realistic arrival process of bots’ requests on an e-commerce Web server. Based on real…
Online Web Bot Detection Using a Sequential Classification Approach
2019
A significant problem nowadays is detection of Web traffic generated by automatic software agents (Web bots). Some studies have dealt with this task by proposing various approaches to Web traffic classification in order to distinguish the traffic stemming from human users' visits from that generated by bots. Most of previous works addressed the problem of offline bot recognition, based on available information on user sessions completed on a Web server. Very few approaches, however, have been proposed to recognize bots online, before the session completes. This paper proposes a novel approach to binary classification of a multivariate data stream incoming on a Web server, in order to recogn…
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 …