The growing number of cyber-attacks against Industrial Control Systems (ICS) in recent years has elevated security concerns due to the potential catastrophic impact. Considering the complex nature of ICS, detecting a cyber-attack in them is extremely challenging and requires advanced methods that can harness multiple data modalities. This research utilizes network and sensor modality data from ICS processed with a deep multi-modal cyber-attack detection model for ICS. Results using the Secure Water Treatment (SWaT) system show that the proposed model can outperform existing single modality models and recent works in the literature by achieving 0.99 precision, 0.98 recall, and 0.98 f-measure, which shows the effectiveness of using both modalities in a combined model for detecting cyber-attacks.

翻译：近年来，针对工业控制系统（ICS）的网络攻击数量不断增加，由于其潜在的灾难性影响，安全问题日益突出。考虑到ICS的复杂性，在其内部检测网络攻击极具挑战性，需要能够利用多种数据模态的先进方法。本研究利用经深度多模态网络攻击检测模型处理的ICS网络与传感器模态数据。基于安全水处理（SWaT）系统的实验结果表明，该模型在性能上优于现有单模态模型及近期文献工作，实现了0.99的精确率、0.98的召回率和0.98的F值，这证明了在组合模型中同时使用两种模态进行网络攻击检测的有效性。