Cyber threat detection in industry 4.0: Leveraging GloVe and self-attention mechanisms in BiLSTM for enhanced intrusion detection

In Industry 4.0 , interconnected systems and real-time communication are vital for seamless operations but expose industrial networks to sophisticated cyber threats. Traditional intrusion detection systems often fail to address modern, evolving attacks. This paper presents a novel cyber threat detection approach using a Bidirectional Long Short-Term Memory (BiLSTM) model integrated with GloVe word embeddings and a self-attention mechanism. GloVe captures global co-occurrence relationships in network events, enhancing contextual representation and detection accuracy. To handle class imbalance , random oversampling balances attack category distributions, followed by Principal Component Analysis (PCA) for feature reduction. The model's parameters are fine-tuned using the Single Candidate Optimization Algorithm (SCOA) and Greylag Goose Optimization Algorithm (GLGOA), improving computational efficiency and detection performance. Evaluation on the CIC-IDS-2018 dataset demonstrates superior accuracy, precision, recall, and F1-score compared to state-of-the-art methods. The model effectively detects intrusions and prioritizes high-risk threats, strengthening cybersecurity in Industry 4.0 environments. This adaptable framework can be enhanced to address more complex attack patterns, ensuring robust protection for critical infrastructures.