A Deep Multi-Modal Cyber-Attack Detection in Industrial Control Systems
This addresses security for industrial systems, but it is incremental as it combines existing modalities for improved detection.
The paper tackled cyber-attack detection in Industrial Control Systems by proposing a deep multi-modal model using network and sensor data, achieving high performance with 0.99 precision, 0.98 recall, and 0.98 f-measure on the SWaT dataset.
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.