## 4 Discussions
### Future Research Directions
Ultimately, considering intentional threats as the only challenges within the field of cyber security leaves a variety of unintentional threats not addressed. From human unintentional actions to software misconfiguration and errors, threats to the cyber security of a system are not always premeditated. When a system such as a power grid goes offline, the business impact of that activity is significant, regardless of whether the root cause was a willful cyber intrusion or an accidental software bug. Subsequently, there is a potential research gap addressing the full spectrum of threats to systems, including unintentional ones, to improve overall system cyber resilience. Many studies in the recent novel cyber security literature introduced machine learning implementations to solve challenges within the space. However, few of these studies comprehensively addressed how the nature of machine learning training data may affect cyber security outcomes, nor how traditional machine learning algorithms can be challenged by novel inputs and data structures. This is sometimes referred to as \"Generalization\", where the algorithm must handle new data scenarios outside of its original scope [62]. Within the scope of cyber security, intrusion detection was one of the primary research areas that utilized machine learning algorithms as a mechanism for identifying and countering cyber-attacks. Intrusion detection that relies on machine learning, however, has the potential to be influenced by training data and algorithm design, and thus such considerations must be considered [63]. Additionally, questions need to be posed regarding the accuracy of intrusion detection mechanisms utilizing machine learning when posed against unique or unseen vulnerability exploitations, such as zero day exploits. Zero-shot and few-shot algorithms may pose part of the solution to address these needs, but were not covered in detail within the most recent cyber security novel literature, and therefore indicate a potential research gap. There is a need for machine learning algorithms designed for cyber security implementations to be tested in environments that may have unpredictable data. Such examples include detecting high-tier threat actor techniques such as the employment of zero days, in addition to complex architecture environments. Cyber security is an arms race between those who wish to breach cyber defenses, and those who wish to strengthen them. Subsequently, there is a research imperative to evaluate security solutions, such as machine learning, against novel scenarios to truly test their robustness and to ensure that they are fit for purpose. Robustness within the novel literature is a theme discussed throughout the research topics. From a modeling and simulation perspective, a need for robustness is identified through the accuracy of models such as digital twins, in addition to the model flexibility and resiliency when faced with unpredictable or complex behaviors. Without such robustness, the value of simulations is significantly lessened, especially in scenarios that involve CPS intraconnectedness with other systems. This is consequently a research need warranting more analysis. Few-shot algorithm applications to intrusion detection scenarios are an opportunity to improve the robustness of complex CPS. There was limited recent novel research in this area, but as CPS become larger and more integrated in a system-of-systems landscape, features of complexity such as nonlinearity and emergence may reduce the accuracy of traditional machine learning models. Subsequently, leveraging emerging research into few-shot and one-shot algorithms that can make accurate predictions on limited training data is a research area that will increase in value as CPS evolve within the modern integrated environment. From a cyber security perspective, recent novel literature often discussed the need to build resilience within CPS networks.