Zarzycki Krzysztof, Chaber Patryk, Cabaj Krzysztof, Ławryńczuk Maciej, Marusak Piotr, Nebeluk Robert, Plamowski Sebastian, Wojtulewicz Andrzej
Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
Sensors (Basel). 2023 Jul 28;23(15):6778. doi: 10.3390/s23156778.
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.
这项工作关注的是网络工业控制系统对网络攻击的脆弱性，这在当今是一个关键问题。这是因为对受控过程的攻击可能会损坏或摧毁它。这些攻击使用长短期记忆（LSTM）神经网络，该网络对动态过程进行建模。这意味着攻击者可能不知道该过程的物理性质；一个LSTM网络就足以误导过程操作员。我们的实验研究是在一个包含磁悬浮过程的工业控制网络中进行的。描述了模型训练、评估和结构选择。所选的LSTM网络很好地模拟了所考虑的过程。最后，基于所获得的结果，我们制定了针对所考虑的网络攻击类型的可能保护方法。
