Accelerating Sketch-Based Computations with GPU: A Case Study for Network Traffic Change Detection

2011 ◽  
Author(s):  
Theophilus Wellem ◽  
Yu-Kuen Lai ◽  
Chun-Chieh Lee ◽  
Kuei-Sheng Yang
Keyword(s):  
Change Detection ◽  
Network Traffic

A case study on the representativeness of public DoS network traffic data for cybersecurity research

2020 ◽  
Author(s):  
Marta Catillo ◽  
Antonio Pecchia ◽  
Massimiliano Rak ◽  
Umberto Villano
Keyword(s):  
Network Traffic ◽  
Traffic Data

scholarly journals Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

2019 ◽  
Vol 11 (9) ◽  
pp. 1123 ◽  
Author(s):  
Jérémie Sublime ◽  
Ekaterina Kalinicheva
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Before And After ◽  
Damage Mapping ◽  
Tohoku Tsunami ◽  
Post Disaster ◽  
Speed Of Analysis

Post-disaster damage mapping is an essential task following tragic events such as hurricanes, earthquakes, and tsunamis. It is also a time-consuming and risky task that still often requires the sending of experts on the ground to meticulously map and assess the damages. Presently, the increasing number of remote-sensing satellites taking pictures of Earth on a regular basis with programs such as Sentinel, ASTER, or Landsat makes it easy to acquire almost in real time images from areas struck by a disaster before and after it hits. While the manual study of such images is also a tedious task, progress in artificial intelligence and in particular deep-learning techniques makes it possible to analyze such images to quickly detect areas that have been flooded or destroyed. From there, it is possible to evaluate both the extent and the severity of the damages. In this paper, we present a state-of-the-art deep-learning approach for change detection applied to satellite images taken before and after the Tohoku tsunami of 2011. We compare our approach with other machine-learning methods and show that our approach is superior to existing techniques due to its unsupervised nature, good performance, and relative speed of analysis.

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