# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 8 Conclusions and Future Work  
We studied how to utilize software networking and virtualization concepts to slice and segment a 5G network into small portions that are easy to manage and monitor. We also proposed an SDN-based monitoring framework that separates different phases of monitoring: information collection, distribution, inferencing, and control actions. The framework provides flexibility and eases application-specific and spatial customizations of security monitoring and control solutions for 5G. Also, to enable more accurate threat detection, our framework supports cross domain (between different 5G stakeholders) exchange of security knowledge and trust measurements. We evaluated the feasibility of our proposals by describing two monitoring scenarios--with cross domain-correlated metrics and with novel control proposals.  
We also argued that monitoring accuracy increases as customized monitoring can focus on few specific data flows. The results we gained from the testbed illustrate that SDNs dedicated to only one application provide more homogenous data flows than SDNs that support more applications. This indicates that application-dedicated software networks can simplify monitoring and autonomous control and thus decrease the numbers of false positives and false negatives in anomaly detection. The initial results on monitoring accuracy should be verified with larger data sets and with different applications. We utilized MDS, \(k\)-means, and bisecting-\(k\)-means algorithms for data analysis. In the future, more work will be needed to evaluate how different learning models, such as \(k\)-means \(|\) 1, spectral clustering, Density-based spatial clustering of applications with noise (DBSCAN), may be applied and which models are suitable for which applications. More work also is needed to understand how well these approaches respond to different kinds of attacks. Specific attention should be given to cross-slice threats where an adversary has access to many applications and slices.  
This work has been performed within the 5G-ENSURE project (www.5 gensure.eu) and funding has been received from the European Union's Horizon 2020 research and innovation programme under grant agreement number 671562. The work has also been supported by the CORNET project (5 qntf.fi/projects/cornet/), partly funded by Business Finland. The authors wish to thank [PERSON] for advice and tools related to machine learning, data normalization, and data visualization, as well as [PERSON] for advice on big data frameworks.  
[PERSON] designed and implemented security monitoring and trust metric enablers. He specified the use cases and performed data analysis. He was also the main contributor for the introduction, related work, discussion and conclusions sections. [PERSON] provided the micro-segmentation enabler for softwarization and virtualization. [PERSON] provided the mobile network testbed and performed the experiments. [PERSON] supported in specifying the softwarization and virtualization concepts and surveyed related work on micro-segmentations. [PERSON] participated to the design of monitoring and trust metric enablers as well as supported the writing process.  
The authors declare no conflict of interest.