Search results for " Classification"
showing 10 items of 1043 documents
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…
Efficient on-the-fly Web bot detection
2021
Abstract A large fraction of traffic on present-day Web servers is generated by bots — intelligent agents able to traverse the Web and execute various advanced tasks. Since bots’ activity may raise concerns about server security and performance, many studies have investigated traffic features discriminating bots from human visitors and developed methods for automated traffic classification. Very few previous works, however, aim at identifying bots on-the-fly, trying to classify active sessions as early as possible. This paper proposes a novel method for binary classification of streams of Web server requests in order to label each active session as “bot” or “human”. A machine learning appro…
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…
Adaptive Learning Process for the Evolution of Ontology-Described Classification Model in Big Data Context
2016
International audience; One of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic HMC was proposed. This process uses ontologies to describe the classification model. To prevent cold start and user overload, the classification process automatically learns the ontology-described classification model from a very large set of unstructured text documen…
OPTIMIZATIONS FOR TENSORIAL BERNSTEIN–BASED SOLVERS BY USING POLYHEDRAL BOUNDS
2010
The tensorial Bernstein basis for multivariate polynomials in n variables has a number 3n of functions for degree 2. Consequently, computing the representation of a multivariate polynomial in the tensorial Bernstein basis is an exponential time algorithm, which makes tensorial Bernstein-based solvers impractical for systems with more than n = 6 or 7 variables. This article describes a polytope (Bernstein polytope) with a number of faces, which allows to bound a sparse, multivariate polynomial expressed in the canonical basis by solving several linear programming problems. We compare the performance of a subdivision solver using domain reductions by linear programming with a solver using a c…
Region-based segmentation on depth images from a 3D reference surface for tree species recognition.
2013
International audience; The aim of the work presented in this paper is to develop a method for the automatic identification of tree species using Terrestrial Light Detection and Ranging (T-LiDAR) data. The approach that we propose analyses depth images built from 3D point clouds corresponding to a 30 cm segment of the tree trunk in order to extract characteristic shape features used for classifying the different tree species using the Random Forest classifier. We will present the method used to transform the 3D point cloud to a depth image and the region based segmentation method used to segment the depth images before shape features are computed on the segmented images. Our approach has be…
Weed detection by aerial imaging: impact of soil, crop and weed spectral mixing
2015
International audience; This study aims to evaluate spectral information potential of images captured with a UAV, for site specific weed management. The image acquisition chain was modeled in order to compute the digital values of image pixels, according to the field conditions and objects lying on the ground surface projected in the pixels. The object spectra are mixed in the same pixel to estimate the impact of the spatial resolution of the image. The classification potential into crop, weed and soil classes was studied usinf simulations based on the present multispectral sensor characteristics and according to different mixing rates.
GW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence
2017
On August 14, 2017 at 10 30:43 UTC, the Advanced Virgo detector and the two Advanced LIGO detectors coherently observed a transient gravitational-wave signal produced by the coalescence of two stellar mass black holes, with a false-alarm rate of 1 in 27 000 years. The signal was observed with a three-detector network matched-filter signal-to-noise ratio of 18. The inferred masses of the initial black holes are 30.5-3.0+5.7M and 25.3-4.2+2.8M (at the 90% credible level). The luminosity distance of the source is 540-210+130 Mpc, corresponding to a redshift of z=0.11-0.04+0.03. A network of three detectors improves the sky localization of the source, reducing the area of the 90% credible regio…
Instability of space-time rainfall regimes in the payment of basin Mono-Couffo and its impact on the flow of 1961 to 2000
2009
International audience; The catchment area of Mono-Couffo complex, is located in the Gulf of Benin, in the southern part of the Republics of Benin and Togo, at the continent-ocean interface. It lies about 526 km from north to south between 06 ° 16 'and 09 ° 20'N and covers an area of 27 870 km ². The analysis of the variation in rainfall totals for the period 1961-2000 in the basin of Mono-Couffo, between Benin and Togo has been a slight decrease in precipitation. This study aims to identify possible changes in the precipitation regime of the basin and their potential impact in the hydrological functioning of river Couffo. The data used are the monthly precipitation of a network of 25 stati…
AN ONTOLOGY-BASED RECOMMENDER SYSTEM USING HIERARCHICAL MULTICLASSIFICATION FOR ECONOMICAL E-NEWS
2014
International audience; This paper focuses on a recommender system of economic news articles. Its objectives are threefold: (i) automatically multi-classify new economic articles, (ii) recommend articles by comparing profiles of users and multi-classification of articles, and (iii) managing the vocabulary of the economic news domain to improve the system based on seamlessly intervention of documentalists. In this paper we focus on the automatic multi-classification of the articles, managed by inference process of ontologies, and the enrichment of the documentalist-oriented ontology which provides the necessary capabilities to the DL reasoner for automatic multi-classification.