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)

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 - http://www.mdpi.com/journal/energies). The publisher for Energies is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/en16010329.

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.

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