Search results for "Web bot detection"
showing 3 items of 3 documents
Time series clustering with different distance measures to tell Web bots and humans apart
2022
The paper deals with the problem of differentiating Web sessions of bots and human users by observing some characteristics of their traffic at the Web server input. We propose an approach to cluster bots’ and humans’ sessions represented as time series. First, sessions are expressed as sequences of HTTP requests coming to the server at specific timestamps; then, they are pre-preprocessed to form time series of limited length. Time series are clustered and the clustering performance is evaluated in terms of the ability to partition bots and humans into separate clusters. The proposed approach is applied to real server log data and validated with the use of different time series distance meas…
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…
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…