Deep Learning Anomaly Classification Using Multi-Attention Residual Blocks for Industrial Control Systems.

Area of Science:

• Cybersecurity
• Network Security
• Industrial Control Systems

Background:

• Industrial control systems (ICSs) are critical infrastructure vulnerable to cyber threats.
• Effective anomaly detection is crucial for maintaining ICS security and operational integrity.
• Existing methods often struggle with the complexity and data imbalance inherent in ICS network traffic.

Purpose of the Study:

• To propose a novel, flow-based deep neural network (DNN) method for classifying anomalies in ICS network packets.
• To enhance the accuracy and robustness of anomaly detection in ICS environments.
• To address challenges such as feature extraction and data imbalance in ICS security.

Main Methods:

• A flow-based approach aggregating network packets to extract relevant features.
• Development of a DNN incorporating multi-attention and residual blocks for effective feature identification and gradient management.
• Training the DNN using the Ranger optimizer and focal loss to optimize performance and handle data imbalance.
• Evaluation using the Electra Modbus dataset.

Main Results:

• The proposed method demonstrates superior performance in anomaly classification compared to related techniques.
• Analysis of the Electra Modbus dataset validates the effectiveness of individual mechanisms within the proposed method.
• Achieved high precision, recall, and F1-score, indicating robust anomaly detection capabilities.

Conclusions:

• The novel flow-based DNN method offers a significant advancement in ICS anomaly detection.
• The combination of multi-attention, residual blocks, Ranger optimizer, and focal loss contributes to a highly effective security solution.
• This approach provides a promising direction for securing critical industrial control systems against cyber threats.