Monday, March 20 2023 Sign In   |    Register

News Quick Search



Front Page
Power News
Today's News
Yesterday's News
Week of Mar 20
Week of Mar 13
Week of Mar 06
Week of Feb 27
Week of Feb 20
By Topic
By News Partner
Gas News
News Customization


Pro Plus(+)

Add on products to your professional subscription.
  • Energy Archive News

    Home > News > Power News > News Article

    Share by Email E-mail Printer Friendly Print

    New Study Findings from AGH University of Science and Technology Illuminate Research in Machine Learning (Internet Threat Detection in Smart Grids Based on Network Traffic Analysis Using LSTM, IF, and SVM)

    January 25, 2023 - Internet Daily News


      2023 JAN 24 (NewsRx) -- By a News Reporter-Staff News Editor at Internet Daily News -- Investigators publish new report on artificial intelligence. According to news reporting originating from Krakow, Poland, by NewsRx correspondents, research stated, “The protection of users of ICT networks, including smart grids, is a challenge whose importance is constantly growing. Internet of Things (IoT) or Internet of Energy (IoE) devices, as well as network resources, store more and more information about users.”

      Financial supporters for this research include Polish Ministry of Science And Higher Education.

      The news editors obtained a quote from the research from AGH University of Science and Technology: “Large institutions use extensive security systems requiring large and expensive resources. For smart grid users, this becomes difficult. Efficient methods are needed to take advantage of limited sets of traffic features. In this paper, machine learning techniques to verify network events for recognition of Internet threats were analyzed, intentionally using a limited number of parameters. The authors considered three machine learning techniques: Long Short-Term Memory, Isolation Forest, and Support Vector Machine. The analysis is based on two datasets. In the paper, the data preparation process is also described. Eight series of results were collected and compared with other studies. The results showed significant differences between the techniques, the size of the datasets, and the balance of the datasets. We also showed that a more accurate classification could be achieved by increasing the number of analyzed features.”

      According to the news editors, the research concluded: “Unfortunately, each increase in the number of elements requires more extensive analysis. The work ends with a description of the steps that can be taken in the future to improve the operation of the models and enable the implementation of the described methods of analysis in practice.”

      For more information on this research see: Internet Threat Detection in Smart Grids Based on Network Traffic Analysis Using LSTM, IF, and SVM. Energies, 2022,16(329):329. (Energies - The publisher for Energies is MDPI AG.

      A free version of this journal article is available at

      Our news journalists report that additional information may be obtained by contacting Szymon Stryczek, Institute of Telecommunications, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland. Additional authors for this research include Marek Natkaniec.

      (Our reports deliver fact-based news of research and discoveries from around the world.)


    Other Articles - International


       Home  -  Feedback  -  Contact Us  -  Safe Sender  -  About Energy Central   
    Copyright © 1996-2023 by CyberTech, Inc. All rights reserved.
    Energy Central® and Energy Central Professional® are registered trademarks of CyberTech, Incorporated. Data and information is provided for informational purposes only, and is not intended for trading purposes. CyberTech does not warrant that the information or services of Energy Central will meet any specific requirements; nor will it be error free or uninterrupted; nor shall CyberTech be liable for any indirect, incidental or consequential damages (including lost data, information or profits) sustained or incurred in connection with the use of, operation of, or inability to use Energy Central. Other terms of use may apply. Membership information is confidential and subject to our privacy agreement.