The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential data, obstruct activity, etc. To this end, intrusion detection systems (IDSs) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparisons with previous attacks. However, with the increased availability of computational power and data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time series format and use predictive models to forecast forthcoming malign packets. We propose a new architecture combining convolutional neural networks, long short-term memory networks, and attention. The findings indicate that our model performs strongly, exhibiting an F1 score and AUC that are within margins of 1% and 3%, respectively, when compared to conventional real-time detection. Also, our architecture achieves an ∼8% F1 score improvement compared to an LSTM (long short-term memory) model.

Safety management is a priority to guarantee human-centered manufacturing processes in the context of Industry 5.0, which aims to realize a safe human–machine environment based on knowledge-driven approaches. The traditional approaches for safety management in the industrial environment include staff training, regular inspections, warning signs, etc. Despite the fact that proactive measures and procedures have exceptional importance in the prevention of safety hazards, human–machine–environment coupling requires more sophisticated approaches able to provide automated, reliable, real-time, cost-effective, and adaptive hazard identification in complex manufacturing processes. In this context, the use of virtual reality (VR) can be exploited not only as a means of human training but also as part of the methodology to generate synthetic datasets for training AI models. In this paper, we propose a flexible and adjustable detection system that aims to enhance safety management in Industry 5.0 manufacturing through real-time monitoring and identification of hazards. The first stage of the system contains the synthetic data generation methodology, aiming to create a synthetic dataset via VR, while the second one concerns the training of AI object detectors for real-time inference. The methodology is evaluated by comparing the performance of models trained on both real-world data from a publicly available dataset and our generated synthetic data. Additionally, through a series of experiments, the optimal ratio of synthetic and real-world images is determined for training the object detector. It has been observed that even with a small amount of real-world data, training a robust AI model is achievable. Finally, we use the proposed methodology to generate a synthetic dataset of four classes as well as to train an AI algorithm for real-time detection.