Integrating Industrial Control Systems (ICS) with Internet of Things (IoT) technologies has amplified the vulnerability of ICS to a larger range of cyber-attacks, posing significant risks to Critical Infrastructures (CI). Recent cyber-attacks on oil and gas sectors and water treatment plants highlight this potential threat. Current attack detection methods rely on unified machine learning techniques, which pose data privacy and transfer challenges. To address these issues, the self-attention-based learning method has become a popular and effective solution for detecting attacks in ICS. This paper presents a novel Hypergraph Attention-based Multilayer Perceptron Neural Network (HATT-MLPNN) for detecting cyber-attacks in ICS environments. The hypergraph-based attention layer helps to optimise the Multilayer Perceptron Neural Network (MLPNN) weights for different feature sets. Integrating hypergraph attention mechanisms into an MLPNN has significantly increased the ability to capture and leverage complex feature interactions in ICS datasets. The proposed model is evaluated on iTrust’s Secure Water Treatment (SWaT) and Mississippi’s Gas Pipeline dataset and experimental evaluations reveal that the training of the proposed attack detection model is faster when trained on labeled data. The model is consistently outperformed with recall and F1- scores on both datasets.