With digitization, critical infrastructures face a higher risk of security incidents and attacks on cyber-physical systems (CPS). In the past 50 years, research and practice have developed various approaches to monitor and detect attacks such as with anomaly detection. While many approaches focuses on artificial neural networks, bio-inspired approaches utilize nature as reference. For example, artificial immune systems (AIS) refer to principles of the natural immune system. In this paper, we investigate the Negative Selection Algorithm (NSA), an algorithm from the domain of AIS for anomaly detection in CPS. Particularly in CPS, datasets can become quite complex and can require a number of detectors for the analysis. Therefore, we will investigate how AIS can be extended to handle and manage complex datasets of CPS. We propose two models that use Principal Component Analysis (PCA) and Autoencoder (AE) to enable dimensionality reduction. Using these models, we are able to show that it is possible to apply the NSA approach to such datasets. Our results indicate that the use of PCA and AE is beneficial for both a better representation of the data and therefore significantly relevant for an improvement of the detection rate, and provides in addition the possibility to add further features to support the identification of anomalies. As the NSA approach allows for distributed computation, it might be possible to allow faster or distributed detection; the extent to which this is possible remains to be investigated and therefore represents future work.