ArticleSensors (Basel, Switzerland)2023
Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Neural Network Method of Analysing Sensor Data to Prevent Illegal Cyberattacks.Sensors (Basel, Switzerland) · 2025Article
- Enhancing resilience of distributed DC microgrids against cyber attacks using a transformer-based Kalman filter estimator.Scientific reports · 2025Article
- Efficient Cyberattack Detection Methods in Industrial Control Systems.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
This work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamical processes. This means that the attacker may not know the physical nature of the process; an LSTM network is sufficient to mislead the process operator. Our experimental studies were conducted in an industrial control network containing a magnetic levitation process. The model training, evaluation, and structure selection are described. The chosen LSTM network very well mimicked the considered process. Finally, based on the obtained results, we formulated possible protection methods against the considered types of cyber-attack.
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Registered trials
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