HTTP-sCAN: Detecting HTTP-flooding attaCk by modeling multi-features of web browsing behavior from noisy dataset

2013 ◽  
Author(s):  
Jin Wang ◽  
Min Zhang ◽  
Xiaolong Yang ◽  
Keping Long ◽  
Chimin Zhou
Keyword(s):  
Web Browsing ◽  
Flooding Attack ◽  
Browsing Behavior

HTTP-sCAN: Detecting HTTP-flooding attack by modeling multi-features of web browsing behavior from noisy web-logs

2015 ◽  
Vol 12 (2) ◽  
pp. 118-128 ◽  
Author(s):  
Jin Wang ◽  
Min Zhang ◽  
Xiaolong Yang ◽  
Keping Long ◽  
Jie Xu
Keyword(s):  
Web Browsing ◽  
Flooding Attack ◽  
Web Logs ◽  
Browsing Behavior

Influencing web-browsing behavior with intriguing and informative hyperlink wording

2005 ◽  
Vol 31 (5) ◽  
pp. 433-445 ◽  
Author(s):  
Carolyn Y. Wei ◽  
Mary B. Evans ◽  
Matthew Eliot ◽  
Jennifer Barrick ◽  
Brandon Maust ◽  
...  
Keyword(s):  
Web Browsing ◽  
Browsing Behavior

scholarly journals Web browsing behavior analysis and interactive hypervideo

2013 ◽  
Vol 7 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Luis A. Leiva ◽  
Roberto Vivó
Keyword(s):  
Behavior Analysis ◽  
Web Browsing ◽  
Browsing Behavior

scholarly journals Reinforcement Learning Page Prediction for Hierarchically Ordered Municipal Websites

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 231
Author(s):  
Petri Puustinen ◽  
Kostas Stefanidis ◽  
Jaana Kekäläinen ◽  
Marko Junkkari
Keyword(s):  
Reinforcement Learning ◽  
Learning Approach ◽  
Electronic Document ◽  
Training Algorithm ◽  
Web Browsing ◽  
Accurate Predictor ◽  
Document Repositories ◽  
Browsing Behavior

Public websites offer information on a variety of topics and services and are accessed by users with varying skills to browse the kind of electronic document repositories. However, the complex website structure and diversity of web browsing behavior create a challenging task for click prediction. This paper presents the results of a novel reinforcement learning approach to model user browsing patterns in a hierarchically ordered municipal website. We study how accurate predictor the browsing history is, when the target pages are not immediate next pages pointed by hyperlinks, but appear a number of levels down the hierarchy. We compare traditional type of baseline classifiers’ performance against our reinforcement learning-based training algorithm.

scholarly journals Online News in India: Appraising the Digital News Consumption Landscape in theWorld’s Largest Democracy (2014-2018)

2021 ◽  
Author(s):  
Subhayan Mukerjee
Keyword(s):  
Online News ◽  
Indian Society ◽  
Web Browsing ◽  
News Consumption ◽  
News Sources ◽  
Internet Users ◽  
Digital News ◽  
Different Types ◽  
Browsing Behavior ◽  
The Web

How do people in the world's largest democracy consume online news? This article reports findings from the analysis of a novel empirical dataset tracking the web-browsing behavior of more than 50,000 Indian internet users over 45 months. In doing so, it seeks to understand the digital news consumption landscape of a crucial, but understudied context and appraise the prominence and longitudinal trends of the audience share of different types of news sources in the online Indian space. It finds that while digital-born media have not contested the hegemony of legacy media, regional vernacular media have suffered significant declines in their audience shares. The article proposes the concept of audience mobility, using it to identify qualitatively distinct dynamics in how vernacular audiences in India have migrated to national vis-à-vis international outlets. The findings are discussed in light of contemporary changes in Indian society that is characterized by increasing digitization and literacy.

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