Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Trellet JOfIntegBioinfo2018 15-2.jpg|240px]]</div>
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Teixeira FutureInternet2018 10-8.png|240px]]</div>
'''"[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data]]"'''
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'''"[[Journal:SCADA system testbed for cybersecurity research using machine learning approach|SCADA system testbed for cybersecurity research using machine learning approach]]"'''
  
The advances made in recent years in the field of structural biology significantly increased the throughput and complexity of data that scientists have to deal with. Combining and [[Data analysis|analyzing]] such heterogeneous amounts of data became a crucial time consumer in the daily tasks of scientists. However, only few efforts have been made to offer scientists an alternative to the standard compartmentalized tools they use to explore their data and that involve a regular back and forth between them. We propose here an integrated pipeline especially designed for immersive environments, promoting direct interactions on semantically linked 2D and 3D heterogeneous data, displayed in a common working space. The creation of a semantic definition describing the content and the context of a molecular scene leads to the creation of an intelligent system where data are (1) combined through pre-existing or inferred links present in our hierarchical definition of the concepts, (2) enriched with suitable and adaptive analyses proposed to the user with respect to the current task and (3) interactively presented in a unique working environment to be explored. ('''[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Full article...]]''')<br />
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This paper presents the development of a [[supervisory control and data acquisition]] (SCADA) system testbed used for [[cybersecurity]] research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments. ('''[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Full article...]]''')<br />
 
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Revision as of 14:44, 22 April 2019

Fig3 Teixeira FutureInternet2018 10-8.png

"SCADA system testbed for cybersecurity research using machine learning approach"

This paper presents the development of a supervisory control and data acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments. (Full article...)

Recently featured:

Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data
A view of programming scalable data analysis: From clouds to exascale
Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital