Book ChapterDOI
Bot or Not? A Case Study on Bot Recognition from Web Session Logs
Stefano Rovetta,Alberto Cabri,Francesco Masulli,Grażyna Suchacka +3 more
- pp 197-206
TLDR
A study of web usage logs is reported on to verify whether it is possible to achieve good recognition rates in the task of distinguishing between human users and automated bots using computational intelligence techniques.Abstract:
This work reports on a study of web usage logs to verify whether it is possible to achieve good recognition rates in the task of distinguishing between human users and automated bots using computational intelligence techniques. Two problem statements are given, offline (for completed sessions) and on-line (for sequences of individual HTTP requests). The former is solved with several standard computational intelligence tools. For the second, a learning version of Wald’s sequential probability ratio test is used.read more
Citations
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Journal ArticleDOI
Bot recognition in a Web store: An approach based on unsupervised learning
TL;DR: Results demonstrate that the classification based on unsupervised learning is very efficient, achieving a similar performance level as the fully supervised classification, and is an experimental indication that the bot recognition problem can be successfully dealt with using methods that are less sensitive to mislabelled data or missing labels.
Journal ArticleDOI
Identifying legitimate Web users and bots with different traffic profiles — an Information Bottleneck approach
Grażyna Suchacka,Jacek Iwański +1 more
TL;DR: This paper proposes a novel approach to identify various profiles of bots and humans which combines feature selection and unsupervised learning of HTTP-level traffic patterns to develop a user session classification model.
Proceedings ArticleDOI
Online Web Bot Detection Using a Sequential Classification Approach
TL;DR: The present approach uses deep neural networks combined with Wald's Sequential Probability Ratio Test to express the relationship between subsequent HTTP requests in an ongoing session and to assess the likelihood of each session being generated by a bot or human before it ends.
Proceedings ArticleDOI
Towards a framework for detecting advanced Web bots
Christos Iliou,Theodoros Kostoulas,Theodora Tsikrika,Vasilis Katos,Stefanos Vrochidis,Yiannis Kompatsiaris +5 more
TL;DR: The proposed framework has significant ability to detect Web bots that do not try to hide their bot identity using HTTP Web logs, and balanced accuracy in a false-positive intolerant server > 95%).
References
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Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Journal ArticleDOI
A possibilistic approach to clustering
TL;DR: An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Book ChapterDOI
Sequential Tests of Statistical Hypotheses
TL;DR: A sequential test of a statistical hypothesis is defined as any statistical test procedure which gives a specific rule, at any stage of the experiment (at the n-th trial for each integral value of n), for making one of the following three decisions: (1) to accept the hypothesis being tested (null hypothesis), (2) to reject the null hypothesis, (3) to continue the experiment by making an additional observation.