Abstract

Industrial Control Systems (ICS) are increasingly vulnerable to sophisticated cyberattacks, posing significant threats to critical infrastructure. Traditional security measures often prove inadequate against advanced persistent threats (APTs) and zero-day exploits. This paper proposes a novel hybrid deep learning architecture for enhanced intrusion detection in ICS environments. The architecture leverages feature fusion techniques to combine diverse network traffic characteristics and employs an attention mechanism to selectively focus on the most relevant features for accurate anomaly detection. The proposed model integrates Convolutional Neural Networks (CNNs) for local pattern extraction and Recurrent Neural Networks (RNNs), specifically Gated Recurrent Units (GRUs), for capturing temporal dependencies in network traffic. Experimental results on a benchmark ICS dataset demonstrate the superior performance of the proposed hybrid model compared to state-of-the-art intrusion detection systems, achieving higher detection accuracy and lower false positive rates. The improved performance highlights the effectiveness of the feature fusion and attention mechanism in enhancing the model's ability to identify subtle and complex attack patterns in ICS networks.