Botnet Behaviour Analysis Using IP Flows: With HTTP Filters Using Classifiers

2014 ◽  
Author(s):  
Fariba Haddadi ◽  
Jillian Morgan ◽  
Eduardo Gomes Filho ◽  
A. Nur Zincir-Heywood
Keyword(s):  
Behaviour Analysis ◽  
Ip Flows

Decision Behaviour, Analysis and Support

2009 ◽  
Author(s):  
Simon French ◽  
John Maule ◽  
Nadia Papamichail
Keyword(s):  
Behaviour Analysis

Towards a distributed data fusion pipeline for pedestrian behaviour analysis

2020 ◽  
Author(s):  
Sara Ferreira ◽  
Thiago RPM Rúbioa ◽  
João Jacob ◽  
Henrique Lopes Cardoso ◽  
Daniel Castro Silva ◽  
...  
Keyword(s):  
Data Fusion ◽  
Distributed Data ◽  
Behaviour Analysis ◽  
Distributed Data Fusion

Behaviour Analysis of DDoS Attack and Its Detection

2018 ◽  
Vol 6 (5) ◽  
pp. 139-144
Author(s):  
Mahadev . ◽  
◽  
◽  
Vinod Kumar
Keyword(s):  
Behaviour Analysis ◽  
Ddos Attack

Behaviour Analysis of Axially Loaded Glued-in Timber-steel Joints

2001 ◽  
Vol 85 (1) ◽  
pp. 1-6
Author(s):  
Massimo Del Senno ◽  
Maurizio Piazza ◽  
Roberto Tomasi
Keyword(s):  
Behaviour Analysis ◽  
Axially Loaded ◽  
Steel Joints

Digital pattern of life based tourist behaviour analysis system

2020 ◽  
Vol 5 ◽  
pp. 78-83
Author(s):  
S.A. Mikhailov ◽  
Keyword(s):  
Neural Networks ◽  
Tourism Industry ◽  
Data Sources ◽  
Tourist Behavior ◽  
Behaviour Analysis ◽  
Travel Experience ◽  
Digital Pattern ◽  
Analysis System ◽  
Tourist Behaviour ◽  
Tourism Services

The tourism industry has grown rapidly in recent years, and IT technology is also having a big impact on tourists. Tourism services, information generated by tourists and other sources can be used to build models of tourist behavior. These models can improve the travel experience in various ways. The author presents the system for analyzing tourist behavior based on the concept of a digital pattern of life. The system determines the tourist, possible data sources, ways of storing and presenting data, as well as tools for analyzing behavior. The author used artifi cial neural networks to analyze behavior from a dataset of tourist travels made with cars. One scenario of tourist behavior using artifi cial neural networks is presented. The collected results will be used for improving tourist services.

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