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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File: | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Teixeira FutureInternet2018 10-8.png|240px]]</div> | ||
'''"[[Journal: | '''"[[Journal:SCADA system testbed for cybersecurity research using machine learning approach|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. ('''[[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
"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