## 4 Designing a Digital Twin for the Factory of the Future
### Fostering FoF Resilience
Much importance is given to the ergonomic evaluation, i.e., to the creation of physical models and their analysis through different real-world experiments [140; 141; 142]. The digital human simulation is also explored by some authors. State-of-the-art systems in this field can be mentioned [129], such as Siemens Tecnomatix Jack [143], Dassault Delmia Human [144] and RAMSIS [145]. To optimize these systems, Motion Capture systems are also used to capture real worker movements, edit and animate them [146; 147; 148]. However, the results of this kind of simulations are not so accurate since, in some way, they try to model humans as technological equipment, but human behavior is very different from a machine. More importantly, detecting human deviations based their behavior in the physical space will most often inform about threats after they materialized into damages. To grow predictive, we must observe user behavior on the network. CRs can provide insights on anomalous user behavior observed on the network and support virtual assessment of incident response strategies before implementation on real infrastructure. The use of UEBA techniques will provide evidence of attack intentions even in preparatory phases and thus enable predicting rather than responding to cyber-threats [121]. UEBA algorithm training requires nothing but real or highly realistic traffic. When it comes to OT traffic, the issue is that such traffic is highly business-specific and in most cases confidential. By consideration for safety, process continuity and confidentiality issues, factory owners generally prohibit any active intervention on their OT network and do not easily share real industrial data at needed scale and representativeness. The algorithm training should thus be done on the actual infrastructure with the required security clearance or based on synthetic data, which a virtual replica of the actual OT could generate in a CR environment. This makes OT CRs a very strategic tool for detection algorithm training [106]. Beyond this, it forms a perfect playground for cyber-defenders to test and improve their response strategies in virtual environment without affecting operational manufacturing systems [47]. The ability to correlate human behavior patterns in the physical and in the cyberspace is key to prevent insider-threat, which accounts for 60% of cyber-attacks to date [50]. In a context where machines grow more autonomous, the relation between human operators and factory automation can substantially change. While some foresee the advent of unmanned factories, the law reminds us that the human remains in any case accountable [40]. With CyberFactory#1, we intend to leverage the complementary strengths of humans and machines by enabling collaboration and mutual learning. While we do that, we also make due considerations to scenarios in which such a relationship turns wrong. Typical scenarios in the context of FoF are lack of vigilance of a human worker, due to extreme automation of his activities leading to cognitive underload (a safety issue) [117]; or deliberate miss-training of a collaborative robot by a human operator for sabotage purpose (a security issue). In addition, we found that skilled cyber-attackers have proven abilities to exploit basic safety mechanisms, known as \"fail open mechanisms,\" to their advantage [139]. To envisage a future where safety and security go hand in hand, we need to leverage the complementary strengths of DTs and CRs. It is expectable that DTs and CRs which have been developed in different environments, by different vendors to fulfill different needs, will not spontaneously be brought to interface, communicate, co-simulate and agree on a common understanding of the risk level of a complete manufacturing ecosystem. Much development work is required to achieve such synergies and such development can hardly be done without proper collaboration of editors from automation and security sectors.